CRAN Package Check Results for Package partialAR

Last updated on 2020-04-25 01:53:10 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.12 25.71 699.60 725.31 OK
r-devel-linux-x86_64-debian-gcc 1.0.12 15.83 629.92 645.75 OK
r-devel-linux-x86_64-fedora-clang 1.0.12 780.76 OK
r-devel-linux-x86_64-fedora-gcc 1.0.12 422.87 OK
r-devel-windows-ix86+x86_64 1.0.12 73.00 350.00 423.00 OK
r-patched-linux-x86_64 1.0.12 18.64 760.72 779.36 OK
r-patched-osx-x86_64 1.0.12 OK
r-patched-solaris-x86 1.0.12 1331.40 OK
r-release-linux-x86_64 1.0.12 18.39 746.99 765.38 ERROR
r-release-windows-ix86+x86_64 1.0.12 66.00 362.00 428.00 ERROR
r-release-osx-x86_64 1.0.12 ERROR
r-oldrel-windows-ix86+x86_64 1.0.12 33.00 461.00 494.00 ERROR
r-oldrel-osx-x86_64 1.0.11 OK

Check Details

Version: 1.0.12
Check: tests
Result: ERROR
     Running ‘tests.R’ [13s/14s]
    Running the tests in ‘tests/tests.R’ failed.
    Complete output:
     > all.tests.pass <- TRUE
     > all.tests.error.count <- 0
     >
     > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) {
     + # expr is a string representing an R expression, and
     + # out is the output that is expected. Prints and evaluates
     + # expr. If out is given and it matches the output of
     + # evaluating expr, returns TRUE. Otherwise, returns FALSE.
     +
     + cat(expr, "-> ")
     +
     + p <- function (v) {
     + if (length(v) < 5) {
     + cat(v)
     + } else {
     + cat(class(v), "(", length(val), ")")
     + }
     + }
     + p(val)
     +
     + result <- all.equal(val, out, tolerance=tol)
     + if (!isTRUE(result)) {
     + if (!missing(out)) {
     + cat(" (Expecting ")
     + p(out)
     + cat(")")
     + }
     + cat("\nERROR: ", result, "\n")
     + all.tests.pass <<- FALSE
     + all.tests.error.count <<- all.tests.error.count + 1
     + } else {
     + cat(" OK\n")
     + }
     +
     + isTRUE(result)
     + }
     >
     > assert <- function (expr, out) {
     + # expr is astring representing an R expression,
     + # and out is the output that is expected. Prints
     + # and evaluates expr. If out matches the output of
     + # evaluating expr, returns TRUE. Otherwise, stops
     + # the execution with an error message.
     + if (!test(expr, out)) {
     + stop("Expression ", deparse(substitute(expr)),
     + " does not evaluate to its expected value\n")
     + }
     + }
     >
     > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) {
     + R <- R0
     + M <- M0
     + X <- numeric()
     + for (i in 1:length(eps_M)) {
     + M <- rho * M + eps_M[i]
     + R <- R + eps_R[i]
     + X[i] <- M + R
     + }
     + X
     + }
     >
     > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548,
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     >
     > data.IBM <- structure(c(176.668606104443, 175.947896814914, 175.113391321774,
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     + 181.929465287177, 183.236825287349, 184.377079122086), .Dim = c(502L,
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     >
     > test_cfit <- function (fast_only=FALSE) {
     + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_)
     + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_)
     +
     + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_))
     + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN))
     + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433))
     + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_))
     +
     + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(-1,1,0)", 1)
     + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_)
     + test("partialAR:::pvmr_par_c(0,0,1)", 0)
     + test("partialAR:::pvmr_par_c(0,1,0)", 1)
     + test("partialAR:::pvmr_par_c(0,1,1)", 2/3)
     + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429)
     + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25)
     + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25)
     +
     + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_)
     + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1)
     + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0)
     + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3)
     +
     + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347)
     +
     + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + }
     >
     >
     > test_lr <- function (fast_only=FALSE) {
     + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference
     + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143)
     + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353)
     +
     + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347)
     +
     + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531)
     + }
     >
     > test.likelihood_ratio.par <- function (fast_only=FALSE) {
     + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184)
     +
     + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347)
     +
     + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE)
     + test("nrow(SAMPLES)", 10)
     + test("sum(SAMPLES$seed)", 55)
     + test("mean(SAMPLES$rw_lrt)", -4.43576369917)
     + test("mean(SAMPLES$mr_lrt)", -3.8960913155)
     + test("mean(SAMPLES$kpss_stat)", 3.7269871366)
     + }
     >
     > test_lr2 <- function(fast_only=FALSE) {
     + test.likelihood_ratio.par(fast_only)
     +
     + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE)
     +
     + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE)
     +
     + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10)
     + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1)
     +
     + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE)
     + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE)
     +
     + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE))
     +
     + print(partialAR:::test.par(data.L))
     + print(partialAR:::test.par(data.L, robust=TRUE))
     +
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW")
     +
     + partialAR:::print.par.lrt(); cat("\n\n")
     + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n")
     +
     + # partialAR:::print.par.lrt.mr(); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n")
     +
     + partialAR:::print.par.lrt.rw(); cat("\n\n")
     + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n")
     +
     + }
     >
     > test_fit.par.both <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.both(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr",
     + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082,
     + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + }
     >
     > test_fit.par.mr <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.mr(data.L)$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.L)$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par",
     + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA,
     + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM)$par",
     + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$stderr",
     + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA,
     + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1))
     +
     + }
     >
     > test_fit.par.rw <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.rw(data.L)$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.L)$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$par",
     + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$stderr",
     + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0))
     + }
     >
     > test_fit.par <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + test("partialAR:::fit.par(data.L, model='ar1')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='ar1')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.L, model='rw')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='rw')$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + }
     >
     > test_fit <- function (fast_only=FALSE) {
     + test("partialAR:::par.rho.cutoff(25)", NA_real_)
     + test("partialAR:::par.rho.cutoff(50)", 0.724)
     + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594)
     + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438)
     +
     + test("partialAR:::estimate.rho.par(numeric())", NA_real_)
     + test("partialAR:::estimate.rho.par(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate.rho.par(x1na)", NA_real_)
     +
     + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN))
     + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433))
     + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     +
     + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0))
     + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3))
     + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429))
     + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25))
     + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25))
     +
     + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_))
     + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0))
     + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1))
     + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3))
     +
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545))
     +
     + test_fit.par.both (fast_only)
     + test_fit.par.mr(fast_only)
     + test_fit.par.rw(fast_only)
     + test_fit.par(fast_only)
     +
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]",
     + structure(list(X = 37.8517816659277, M = 0.00867470536387833,
     + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289),
     + .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") )
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]",
     + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997,
     + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") )
     +
     + print(partialAR:::fit.par(data.L))
     + print(partialAR:::fit.par(data.IBM))
     +
     + test("as.data.frame(partialAR:::fit.par(data.L))",
     + structure(list(robust = FALSE, nu = 5,
     + opt_method = "css",
     + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798,
     + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997,
     + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534,
     + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426,
     + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust",
     + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0",
     + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda",
     + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") )
     + }
     >
     > test_par <- function (fast_only=FALSE) {
     + # Comprehensive unit testing for PAR package
     +
     + options(warn=1)
     +
     + test_cfit(fast_only)
     + test_lr(fast_only)
     + test_fit(fast_only)
     + test_lr2(fast_only)
     +
     + if (all.tests.pass) {
     + cat("SUCCESS! All tests passed.\n")
     + } else {
     + stop("ERRORS! ", all.tests.error.count," tests failed\n")
     + }
     + }
     >
     > test_par(TRUE)
     partialAR:::estimate_rho_par_c(numeric()) -> NA OK
     partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK
     partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK
     partialAR:::estimate_rho_par_c(x1na) -> NA OK
     partialAR:::estimate_par_c(numeric()) -> NA NA NA OK
     partialAR:::estimate_par_c(rep(0,5)) -> NA NA NA OK
     partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate_par_c(x1na) -> NA NA NA OK
     partialAR:::pvmr_par_c(0,0,0) -> NA OK
     partialAR:::pvmr_par_c(-1,1,0) -> 1 OK
     partialAR:::pvmr_par_c(1,-1,0) -> NA OK
     partialAR:::pvmr_par_c(1,1,-1) -> NA OK
     partialAR:::pvmr_par_c(0,0,1) -> 0 OK
     partialAR:::pvmr_par_c(0,1,0) -> 1 OK
     partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK
     partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK
     partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK
     partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK
     partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK
     partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK
     partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK
     partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK
     partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK
     partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK
     partialAR:::par.rho.cutoff(25) -> NA OK
     partialAR:::par.rho.cutoff(50) -> 0.724 OK
     partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK
     partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK
     partialAR:::estimate.rho.par(numeric()) -> NA OK
     partialAR:::estimate.rho.par(rep(0,5)) -> NA OK
     partialAR:::estimate.rho.par(x1) -> 0.8497954 OK
     partialAR:::estimate.rho.par(x1na) -> NA OK
     partialAR:::estimate.par(numeric()) -> NA NA NA OK
     partialAR:::estimate.par(rep(0,5)) -> NA NA NA OK
     partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate.par(x1na) -> NA NA NA OK
     partialAR:::pvmr.par(0,0,0) -> NaN OK
     partialAR:::pvmr.par(-1,1,0) -> 1 OK
     partialAR:::pvmr.par(1,-1,0) -> NA OK
     partialAR:::pvmr.par(1,1,-1) -> NA OK
     partialAR:::pvmr.par(0,0,1) -> 0 OK
     partialAR:::pvmr.par(0,1,0) -> 1 OK
     partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman.gain.par(0,0,0) -> NA NA OK
     partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK
     partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK
     partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK
     partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK
     partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK
     partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2)
     (0.0494) (0.0306)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2)
     (0.0508)
     M_0 = 0.0000, R_0 = 37.8349
     (NA) (0.3828)
     Proportion of variance attributable to mean reversion (pvmr) = 0.7673
     Negative log likelihood = 238.53
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2)
     (0.0182) (0.1531)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2)
     (0.5998)
     M_0 = 0.0000, R_0 = 177.4729
     (NA) (2.1228)
     Proportion of variance attributable to mean reversion (pvmr) = 0.9493
     Negative log likelihood = 1076.49
     as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 ))
     ERROR: Component "opt_method": 'current' is not a factor
     partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK
     partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK
     nrow(SAMPLES) -> 10 OK
     sum(SAMPLES$seed) -> 55 OK
     mean(SAMPLES$rw_lrt) -> -4.435764 OK
     mean(SAMPLES$mr_lrt) -> -3.896091 OK
     mean(SAMPLES$kpss_stat) -> 3.726987 OK
     partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK
     partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK
     partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK
     partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) :
     Sample size too small (49) to provide accurate p-value
     1 OK
     partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Random Walk -4.45 0.014
     AR(1) -4.45 0.010
     Combined 0.010
    
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1)
     [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Robust RW -2.65 0.071
     Robust AR(1) -2.65 0.010
     Combined 0.060
    
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     PAR OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RRW OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RW OK
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7
     n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4
     n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1
     n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0
     n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0
     n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0
    
    
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
     Robust Model
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8
     n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6
     n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3
     n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1
     n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0
     n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0
    
    
     \begin{table}
     \begin{tabular}{crrr|rrr}
     & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\
     n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\
     n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\
     n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\
     n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\
     n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\
     \end{tabular}
     \caption{Critical Values for Likelihood Ratio Tests}
     \caption*{For each sample size, 40,000 random walks were generated, and then the
     likelihood ratios were calculated under the hypothesis of a random walk
     (left panel) and under the hypothesis of an AR(1) series (right panel).
     For the hypothesis of an AR(1) series, it was found that the critical values
     depend upon the value of $\rho$, and that as $\rho$ increases, the critical values
     for a given quantile decrease. Thus, by using the limiting case of a random walk
     when computing critical values for the AR(1) case, a conservative estimate is
     obtained.}
     \end{table}
    
     Critical Values for Likelihood Ratio Tests
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.7 -2.9 -2.2
     n=100 -4.7 -3.0 -2.2
     n=250 -4.6 -3.0 -2.2
     n=500 -4.7 -3.2 -2.4
     n=1000 -4.8 -3.1 -2.4
     n=2500 -4.8 -3.1 -2.4
    
    
     Critical Values for Likelihood Ratio Tests
     Robust Model
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.5 -2.9 -2.2
     n=100 -4.6 -2.9 -2.2
     n=250 -4.6 -2.9 -2.3
     n=500 -4.6 -3.0 -2.3
     n=1000 -4.5 -3.0 -2.4
     n=2500 -4.7 -3.1 -2.4
    
    
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-release-linux-x86_64

Version: 1.0.12
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'tests.R' [13s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     > all.tests.pass <- TRUE
     > all.tests.error.count <- 0
     >
     > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) {
     + # expr is a string representing an R expression, and
     + # out is the output that is expected. Prints and evaluates
     + # expr. If out is given and it matches the output of
     + # evaluating expr, returns TRUE. Otherwise, returns FALSE.
     +
     + cat(expr, "-> ")
     +
     + p <- function (v) {
     + if (length(v) < 5) {
     + cat(v)
     + } else {
     + cat(class(v), "(", length(val), ")")
     + }
     + }
     + p(val)
     +
     + result <- all.equal(val, out, tolerance=tol)
     + if (!isTRUE(result)) {
     + if (!missing(out)) {
     + cat(" (Expecting ")
     + p(out)
     + cat(")")
     + }
     + cat("\nERROR: ", result, "\n")
     + all.tests.pass <<- FALSE
     + all.tests.error.count <<- all.tests.error.count + 1
     + } else {
     + cat(" OK\n")
     + }
     +
     + isTRUE(result)
     + }
     >
     > assert <- function (expr, out) {
     + # expr is astring representing an R expression,
     + # and out is the output that is expected. Prints
     + # and evaluates expr. If out matches the output of
     + # evaluating expr, returns TRUE. Otherwise, stops
     + # the execution with an error message.
     + if (!test(expr, out)) {
     + stop("Expression ", deparse(substitute(expr)),
     + " does not evaluate to its expected value\n")
     + }
     + }
     >
     > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) {
     + R <- R0
     + M <- M0
     + X <- numeric()
     + for (i in 1:length(eps_M)) {
     + M <- rho * M + eps_M[i]
     + R <- R + eps_R[i]
     + X[i] <- M + R
     + }
     + X
     + }
     >
     > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548,
     + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909,
     + 37.900978158865, 37.6156384998289, 37.4188525280799, 37.7632279786407,
     + 37.9108174574525, 37.9403353532148, 38.314228699538, 37.8222637701654,
     + 37.5664420068916, 37.3401381393802, 37.0252805845818, 36.7202623283708,
     + 36.7104230297833, 37.2417451535057, 37.3893346323175, 37.9895318461521,
     + 37.7632279786407, 37.7435493814658, 37.8714602631026, 37.5861206040665,
     + 37.487727618192, 37.8025851729905, 37.5369241111293, 36.985923390232,
     + 37.4582097224297, 37.6845135899411, 38.1076034292015, 38.0879248320266,
     + 38.5405325670494, 38.511014671287, 38.6389255529239, 38.7798536105174,
     + 38.5728963231423, 38.6615923034459, 38.3068083822315, 38.2870981643863,
     + 37.6070956487254, 37.6563711933385, 37.7647773914873, 38.0899959859339,
     + 38.0111551145529, 38.7305780659043, 38.4546350160709, 38.9868108978925,
     + 38.9079700265115, 39.1050722049639, 39.1247824228092, 38.7699985015948,
     + 38.2378226197732, 38.6221718677554, 39.2824641655711, 39.1149273138865,
     + 39.0557966603508, 38.8981149175889, 39.2923192744937, 39.7850747206248,
     + 39.4795663440236, 39.1346375317318, 38.9966660068151, 38.4349247982256,
     + 37.8337631539457, 38.2279675108506, 38.8586944818984, 38.346228817922,
     + 38.6813025212912, 39.3415948191068, 39.0755068781961, 38.9769557889698,
     + 39.2627539477259, 39.0459415514282, 39.6569583046307, 40.0511626615356,
     + 40.4552221273631, 40.4158016916726, 40.5340629987441, 40.8888469199585,
     + 40.6720345236608, 40.5439181076667, 40.1792790775297, 40.1300035329166,
     + 40.3172506024464, 40.1694239686071, 40.40594658275, 40.0511626615356,
     + 39.5288418886367, 39.1346375317318, 38.5433309963745, 38.1688368573148,
     + 37.7647773914873, 38.3955043625351, 38.6320269766781, 38.6517371945233,
     + 38.7995638283626, 38.6517371945233, 39.0853619871187, 38.2690477622191,
     + 38.3874972265335, 37.8643454258119, 37.8051206936547, 38.0025364675119,
     + 39.0192277028765, 39.0488400689551, 39.3548345184338, 39.0093569141837,
     + 39.1574187445766, 38.7231040420907, 39.196901899348, 39.9372110513125,
     + 40.183980768634, 40.3419133877198, 40.3813965424912, 39.3252221523552,
     + 39.552250292291, 38.8119411403264, 38.8316827177122, 39.335092941048,
     + 39.621345813141, 40.3813965424912, 40.4801044294198, 40.8058404562842,
     + 40.0655313043197, 39.976694206084, 39.6805705452981, 39.4338008279766,
     + 39.8286323756911, 39.5719918696767, 40.3715257537984, 40.1642391912483,
     + 40.1938515573269, 40.4899752181127, 40.4603628520341, 40.0260481495483,
     + 39.9470818400054, 39.7792784322267, 39.7792784322267, 40.4603628520341,
     + 41.1611888492272, 39.0290984915694, 39.0784524350337, 38.9402613933336,
     + 38.9501321820265, 39.8286323756911, 39.8977278965411, 40.0556605156268,
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     >
     > data.IBM <- structure(c(176.668606104443, 175.947896814914, 175.113391321774,
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     + 178.102712267041, 178.572021126769, 178.210262214062, 179.862620491023,
     + 179.051107254409, 180.048388581333, 178.953334575299, 180.859901817947,
     + 182.443819219531, 186.452499063047, 186.48183086678, 187.889757445966,
     + 188.847929701246, 187.879980178055, 190.089642725945, 189.08258413111,
     + 185.787644845098, 186.736039832467, 185.738758505543, 185.249895109992,
     + 185.983190203318, 182.756691792684, 181.055447176167, 182.228719325489,
     + 180.840347282125, 179.764847811913, 179.999502241778, 177.956053248376,
     + 174.739332105652, 177.281421762516, 180.654579191816, 182.013619431447,
     + 182.805578132239, 180.547029244794, 182.570923702374, 170.935974888267,
     + 169.909361757611, 169.009853109797, 171.072856639021, 171.855038071902,
     + 173.839823457838, 172.910983006292, 173.399846401843, 178.063603195397,
     + 176.137481416927, 175.218418233291, 175.237972769113, 176.254808631859,
     + 173.888709797393, 176.139728143555, 176.935939872984, 176.926110098546,
     + 179.766914910951, 179.953680625262, 180.425509798257, 179.108320023647,
     + 180.071637918511, 181.329849046496, 182.096571452612, 182.037592805988,
     + 180.995636715625, 178.213810549844, 175.893983782621, 174.291730549326,
     + 175.923473105933, 176.621387090987, 174.458836714762, 173.082668293528,
     + 172.748455962656, 173.082668293528, 174.645602429072, 174.439177165887,
     + 174.104964835016, 172.217648143038, 170.418799420996, 169.858502278064,
     + 174.822538368945, 172.768115511531, 175.658069196123, 177.152194910606,
     + 176.955599421859, 179.127979572521, 180.101127241823, 182.194869196986,
     + 181.929465287177, 183.236825287349, 184.377079122086), .Dim = c(502L,
     + 1L), .Dimnames = list(NULL, "IBM"), index = structure(c(15342,
     + 15343, 15344, 15345, 15348, 15349, 15350, 15351, 15352, 15356,
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     + 15737, 15740, 15741, 15742, 15743, 15744, 15747, 15748, 15749,
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     + 15764, 15765, 15768, 15769, 15770, 15771, 15772, 15775, 15776,
     + 15777, 15778, 15779, 15782, 15783, 15784, 15785, 15786, 15789,
     + 15790, 15791, 15792, 15796, 15797, 15798, 15799, 15800, 15803,
     + 15804, 15805, 15806, 15807, 15810, 15811, 15812, 15813, 15814,
     + 15817, 15818, 15819, 15820, 15821, 15824, 15825, 15826, 15827,
     + 15828, 15831, 15832, 15833, 15834, 15835, 15838, 15839, 15840,
     + 15841, 15842, 15845, 15846, 15847, 15848, 15849, 15853, 15854,
     + 15855, 15856, 15859, 15860, 15861, 15862, 15863, 15866, 15867,
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     + 15895, 15896, 15897, 15898, 15901, 15902, 15903, 15904, 15905,
     + 15908, 15909, 15910, 15911, 15912, 15915, 15916, 15917, 15918,
     + 15919, 15922, 15923, 15924, 15925, 15926, 15929, 15930, 15931,
     + 15932, 15933, 15936, 15937, 15938, 15939, 15940, 15943, 15944,
     + 15945, 15946, 15947, 15951, 15952, 15953, 15954, 15957, 15958,
     + 15959, 15960, 15961, 15964, 15965, 15966, 15967, 15968, 15971,
     + 15972, 15973, 15974, 15975, 15978, 15979, 15980, 15981, 15982,
     + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995,
     + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008,
     + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021,
     + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034,
     + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048,
     + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059,
     + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo")
     >
     > test_cfit <- function (fast_only=FALSE) {
     + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_)
     + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_)
     +
     + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_))
     + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN))
     + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433))
     + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_))
     +
     + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(-1,1,0)", 1)
     + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_)
     + test("partialAR:::pvmr_par_c(0,0,1)", 0)
     + test("partialAR:::pvmr_par_c(0,1,0)", 1)
     + test("partialAR:::pvmr_par_c(0,1,1)", 2/3)
     + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429)
     + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25)
     + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25)
     +
     + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_)
     + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1)
     + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0)
     + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3)
     +
     + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347)
     +
     + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + }
     >
     >
     > test_lr <- function (fast_only=FALSE) {
     + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference
     + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143)
     + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353)
     +
     + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347)
     +
     + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531)
     + }
     >
     > test.likelihood_ratio.par <- function (fast_only=FALSE) {
     + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184)
     +
     + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347)
     +
     + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE)
     + test("nrow(SAMPLES)", 10)
     + test("sum(SAMPLES$seed)", 55)
     + test("mean(SAMPLES$rw_lrt)", -4.43576369917)
     + test("mean(SAMPLES$mr_lrt)", -3.8960913155)
     + test("mean(SAMPLES$kpss_stat)", 3.7269871366)
     + }
     >
     > test_lr2 <- function(fast_only=FALSE) {
     + test.likelihood_ratio.par(fast_only)
     +
     + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE)
     +
     + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE)
     +
     + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10)
     + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1)
     +
     + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE)
     + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE)
     +
     + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE))
     +
     + print(partialAR:::test.par(data.L))
     + print(partialAR:::test.par(data.L, robust=TRUE))
     +
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW")
     +
     + partialAR:::print.par.lrt(); cat("\n\n")
     + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n")
     +
     + # partialAR:::print.par.lrt.mr(); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n")
     +
     + partialAR:::print.par.lrt.rw(); cat("\n\n")
     + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n")
     +
     + }
     >
     > test_fit.par.both <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.both(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr",
     + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082,
     + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + }
     >
     > test_fit.par.mr <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.mr(data.L)$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.L)$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par",
     + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA,
     + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM)$par",
     + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$stderr",
     + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA,
     + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1))
     +
     + }
     >
     > test_fit.par.rw <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.rw(data.L)$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.L)$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$par",
     + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$stderr",
     + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0))
     + }
     >
     > test_fit.par <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + test("partialAR:::fit.par(data.L, model='ar1')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='ar1')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.L, model='rw')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='rw')$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + }
     >
     > test_fit <- function (fast_only=FALSE) {
     + test("partialAR:::par.rho.cutoff(25)", NA_real_)
     + test("partialAR:::par.rho.cutoff(50)", 0.724)
     + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594)
     + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438)
     +
     + test("partialAR:::estimate.rho.par(numeric())", NA_real_)
     + test("partialAR:::estimate.rho.par(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate.rho.par(x1na)", NA_real_)
     +
     + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN))
     + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433))
     + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     +
     + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0))
     + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3))
     + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429))
     + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25))
     + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25))
     +
     + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_))
     + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0))
     + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1))
     + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3))
     +
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545))
     +
     + test_fit.par.both (fast_only)
     + test_fit.par.mr(fast_only)
     + test_fit.par.rw(fast_only)
     + test_fit.par(fast_only)
     +
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]",
     + structure(list(X = 37.8517816659277, M = 0.00867470536387833,
     + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289),
     + .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") )
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]",
     + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997,
     + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") )
     +
     + print(partialAR:::fit.par(data.L))
     + print(partialAR:::fit.par(data.IBM))
     +
     + test("as.data.frame(partialAR:::fit.par(data.L))",
     + structure(list(robust = FALSE, nu = 5,
     + opt_method = "css",
     + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798,
     + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997,
     + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534,
     + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426,
     + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust",
     + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0",
     + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda",
     + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") )
     + }
     >
     > test_par <- function (fast_only=FALSE) {
     + # Comprehensive unit testing for PAR package
     +
     + options(warn=1)
     +
     + test_cfit(fast_only)
     + test_lr(fast_only)
     + test_fit(fast_only)
     + test_lr2(fast_only)
     +
     + if (all.tests.pass) {
     + cat("SUCCESS! All tests passed.\n")
     + } else {
     + stop("ERRORS! ", all.tests.error.count," tests failed\n")
     + }
     + }
     >
     > test_par(TRUE)
     partialAR:::estimate_rho_par_c(numeric()) -> NA OK
     partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK
     partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK
     partialAR:::estimate_rho_par_c(x1na) -> NA OK
     partialAR:::estimate_par_c(numeric()) -> NA NA NA OK
     partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK
     partialAR:::pvmr_par_c(0,0,0) -> NA OK
     partialAR:::pvmr_par_c(-1,1,0) -> 1 OK
     partialAR:::pvmr_par_c(1,-1,0) -> NA OK
     partialAR:::pvmr_par_c(1,1,-1) -> NA OK
     partialAR:::pvmr_par_c(0,0,1) -> 0 OK
     partialAR:::pvmr_par_c(0,1,0) -> 1 OK
     partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK
     partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK
     partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK
     partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK
     partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK
     partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK
     partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK
     partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK
     partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK
     partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK
     partialAR:::par.rho.cutoff(25) -> NA OK
     partialAR:::par.rho.cutoff(50) -> 0.724 OK
     partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK
     partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK
     partialAR:::estimate.rho.par(numeric()) -> NA OK
     partialAR:::estimate.rho.par(rep(0,5)) -> NA OK
     partialAR:::estimate.rho.par(x1) -> 0.8497954 OK
     partialAR:::estimate.rho.par(x1na) -> NA OK
     partialAR:::estimate.par(numeric()) -> NA NA NA OK
     partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate.par(x1na) -> NA NaN NaN OK
     partialAR:::pvmr.par(0,0,0) -> NaN OK
     partialAR:::pvmr.par(-1,1,0) -> 1 OK
     partialAR:::pvmr.par(1,-1,0) -> NA OK
     partialAR:::pvmr.par(1,1,-1) -> NA OK
     partialAR:::pvmr.par(0,0,1) -> 0 OK
     partialAR:::pvmr.par(0,1,0) -> 1 OK
     partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman.gain.par(0,0,0) -> NA NA OK
     partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK
     partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK
     partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK
     partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK
     partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK
     partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK
     partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK
     partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2)
     (0.0494) (0.0306)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2)
     (0.0508)
     M_0 = 0.0000, R_0 = 37.8349
     (NA) (0.3828)
     Proportion of variance attributable to mean reversion (pvmr) = 0.7673
     Negative log likelihood = 238.53
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2)
     (0.0182) (0.1531)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2)
     (0.5998)
     M_0 = 0.0000, R_0 = 177.4729
     (NA) (2.1228)
     Proportion of variance attributable to mean reversion (pvmr) = 0.9493
     Negative log likelihood = 1076.49
     as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 ))
     ERROR: Component "opt_method": 'current' is not a factor
     partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK
     partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK
     nrow(SAMPLES) -> 10 OK
     sum(SAMPLES$seed) -> 55 OK
     mean(SAMPLES$rw_lrt) -> -4.435764 OK
     mean(SAMPLES$mr_lrt) -> -3.896091 OK
     mean(SAMPLES$kpss_stat) -> 3.726987 OK
     partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK
     partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK
     partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK
     partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) :
     Sample size too small (49) to provide accurate p-value
     1 OK
     partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Random Walk -4.45 0.014
     AR(1) -4.45 0.010
     Combined 0.010
    
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1)
     [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Robust RW -2.65 0.071
     Robust AR(1) -2.65 0.010
     Combined 0.060
    
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     PAR OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RRW OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RW OK
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7
     n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4
     n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1
     n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0
     n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0
     n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0
    
    
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
     Robust Model
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8
     n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6
     n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3
     n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1
     n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0
     n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0
    
    
     \begin{table}
     \begin{tabular}{crrr|rrr}
     & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\
     n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\
     n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\
     n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\
     n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\
     n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\
     \end{tabular}
     \caption{Critical Values for Likelihood Ratio Tests}
     \caption*{For each sample size, 40,000 random walks were generated, and then the
     likelihood ratios were calculated under the hypothesis of a random walk
     (left panel) and under the hypothesis of an AR(1) series (right panel).
     For the hypothesis of an AR(1) series, it was found that the critical values
     depend upon the value of $\rho$, and that as $\rho$ increases, the critical values
     for a given quantile decrease. Thus, by using the limiting case of a random walk
     when computing critical values for the AR(1) case, a conservative estimate is
     obtained.}
     \end{table}
    
     Critical Values for Likelihood Ratio Tests
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.7 -2.9 -2.2
     n=100 -4.7 -3.0 -2.2
     n=250 -4.6 -3.0 -2.2
     n=500 -4.7 -3.2 -2.4
     n=1000 -4.8 -3.1 -2.4
     n=2500 -4.8 -3.1 -2.4
    
    
     Critical Values for Likelihood Ratio Tests
     Robust Model
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.5 -2.9 -2.2
     n=100 -4.6 -2.9 -2.2
     n=250 -4.6 -2.9 -2.3
     n=500 -4.6 -3.0 -2.3
     n=1000 -4.5 -3.0 -2.4
     n=2500 -4.7 -3.1 -2.4
    
    
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-release-windows-ix86+x86_64

Version: 1.0.12
Check: running tests for arch ‘x64’
Result: ERROR
     Running 'tests.R' [13s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     > all.tests.pass <- TRUE
     > all.tests.error.count <- 0
     >
     > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) {
     + # expr is a string representing an R expression, and
     + # out is the output that is expected. Prints and evaluates
     + # expr. If out is given and it matches the output of
     + # evaluating expr, returns TRUE. Otherwise, returns FALSE.
     +
     + cat(expr, "-> ")
     +
     + p <- function (v) {
     + if (length(v) < 5) {
     + cat(v)
     + } else {
     + cat(class(v), "(", length(val), ")")
     + }
     + }
     + p(val)
     +
     + result <- all.equal(val, out, tolerance=tol)
     + if (!isTRUE(result)) {
     + if (!missing(out)) {
     + cat(" (Expecting ")
     + p(out)
     + cat(")")
     + }
     + cat("\nERROR: ", result, "\n")
     + all.tests.pass <<- FALSE
     + all.tests.error.count <<- all.tests.error.count + 1
     + } else {
     + cat(" OK\n")
     + }
     +
     + isTRUE(result)
     + }
     >
     > assert <- function (expr, out) {
     + # expr is astring representing an R expression,
     + # and out is the output that is expected. Prints
     + # and evaluates expr. If out matches the output of
     + # evaluating expr, returns TRUE. Otherwise, stops
     + # the execution with an error message.
     + if (!test(expr, out)) {
     + stop("Expression ", deparse(substitute(expr)),
     + " does not evaluate to its expected value\n")
     + }
     + }
     >
     > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) {
     + R <- R0
     + M <- M0
     + X <- numeric()
     + for (i in 1:length(eps_M)) {
     + M <- rho * M + eps_M[i]
     + R <- R + eps_R[i]
     + X[i] <- M + R
     + }
     + X
     + }
     >
     > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548,
     + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909,
     + 37.900978158865, 37.6156384998289, 37.4188525280799, 37.7632279786407,
     + 37.9108174574525, 37.9403353532148, 38.314228699538, 37.8222637701654,
     + 37.5664420068916, 37.3401381393802, 37.0252805845818, 36.7202623283708,
     + 36.7104230297833, 37.2417451535057, 37.3893346323175, 37.9895318461521,
     + 37.7632279786407, 37.7435493814658, 37.8714602631026, 37.5861206040665,
     + 37.487727618192, 37.8025851729905, 37.5369241111293, 36.985923390232,
     + 37.4582097224297, 37.6845135899411, 38.1076034292015, 38.0879248320266,
     + 38.5405325670494, 38.511014671287, 38.6389255529239, 38.7798536105174,
     + 38.5728963231423, 38.6615923034459, 38.3068083822315, 38.2870981643863,
     + 37.6070956487254, 37.6563711933385, 37.7647773914873, 38.0899959859339,
     + 38.0111551145529, 38.7305780659043, 38.4546350160709, 38.9868108978925,
     + 38.9079700265115, 39.1050722049639, 39.1247824228092, 38.7699985015948,
     + 38.2378226197732, 38.6221718677554, 39.2824641655711, 39.1149273138865,
     + 39.0557966603508, 38.8981149175889, 39.2923192744937, 39.7850747206248,
     + 39.4795663440236, 39.1346375317318, 38.9966660068151, 38.4349247982256,
     + 37.8337631539457, 38.2279675108506, 38.8586944818984, 38.346228817922,
     + 38.6813025212912, 39.3415948191068, 39.0755068781961, 38.9769557889698,
     + 39.2627539477259, 39.0459415514282, 39.6569583046307, 40.0511626615356,
     + 40.4552221273631, 40.4158016916726, 40.5340629987441, 40.8888469199585,
     + 40.6720345236608, 40.5439181076667, 40.1792790775297, 40.1300035329166,
     + 40.3172506024464, 40.1694239686071, 40.40594658275, 40.0511626615356,
     + 39.5288418886367, 39.1346375317318, 38.5433309963745, 38.1688368573148,
     + 37.7647773914873, 38.3955043625351, 38.6320269766781, 38.6517371945233,
     + 38.7995638283626, 38.6517371945233, 39.0853619871187, 38.2690477622191,
     + 38.3874972265335, 37.8643454258119, 37.8051206936547, 38.0025364675119,
     + 39.0192277028765, 39.0488400689551, 39.3548345184338, 39.0093569141837,
     + 39.1574187445766, 38.7231040420907, 39.196901899348, 39.9372110513125,
     + 40.183980768634, 40.3419133877198, 40.3813965424912, 39.3252221523552,
     + 39.552250292291, 38.8119411403264, 38.8316827177122, 39.335092941048,
     + 39.621345813141, 40.3813965424912, 40.4801044294198, 40.8058404562842,
     + 40.0655313043197, 39.976694206084, 39.6805705452981, 39.4338008279766,
     + 39.8286323756911, 39.5719918696767, 40.3715257537984, 40.1642391912483,
     + 40.1938515573269, 40.4899752181127, 40.4603628520341, 40.0260481495483,
     + 39.9470818400054, 39.7792784322267, 39.7792784322267, 40.4603628520341,
     + 41.1611888492272, 39.0290984915694, 39.0784524350337, 38.9402613933336,
     + 38.9501321820265, 39.8286323756911, 39.8977278965411, 40.0556605156268,
     + 39.9372110513125, 39.9470818400054, 39.9865649947768, 39.9372110513125,
     + 39.9668234173911, 39.9174694739268, 39.9964357834697, 39.9569526286982,
     + 40.0852728817054, 39.9668234173911, 39.9174694739268, 39.2561266315052,
     + 39.7101829113767, 39.8977278965411, 39.7003121226839, 39.9108897674813,
     + 39.7922544746377, 40.1877054507832, 40.3261132924341, 40.464521134085,
     + 40.464521134085, 40.7116779941759, 40.7413368173868, 41.008266226285,
     + 40.9094034822486, 41.8387132761905, 42.204505429125, 41.8090544529796,
     + 41.5717838672923, 41.2455368119723, 40.9687211286705, 40.9588348542668,
     + 41.0379250494959, 40.7709956405977, 40.4941799572959, 40.8105407382123,
     + 40.790768189405, 41.0774701471105, 41.0576975983032, 40.8204270126159,
     + 41.4828073976596, 41.4828073976596, 41.6014426905032, 41.3246270072014,
     + 41.0774701471105, 41.1367877935323, 41.008266226285, 41.2158779887614,
     + 41.6212152393105, 42.204505429125, 42.6790466004996, 42.0265524898596,
     + 41.9672348434378, 41.334513281605, 41.5421250440814, 41.9178034714196,
     + 41.9079171970159, 41.7991681785759, 42.4318897404087, 41.6805328857323,
     + 41.6904191601359, 41.8485995505941, 40.7314505429832, 40.1580466275722,
     + 40.6622466221577, 40.5238387805068, 40.1481603531686, 39.4660074193177,
     + 39.3770309496849, 40.2569093716086, 40.3755446644523, 40.2667956460122,
     + 40.3755446644523, 40.8303132870195, 40.6227015245432, 40.2766819204159,
     + 40.4479793605475, 40.5667983941647, 40.4776841189518, 40.3192587407954,
     + 40.5469952218952, 40.6559126693777, 40.8737475643427, 40.8737475643427,
     + 40.6361094971081, 41.0915824593077, 41.0321729424991, 40.5866015664343,
     + 40.249947637852, 40.6262079109734, 41.2302046651945, 40.7648301168602,
     + 40.8341412198036, 40.4974872912213, 40.339061913065, 40.1311286042347,
     + 40.0915222596956, 39.7845730895177, 40.3489634991997, 41.121287217712,
     + 41.2995157681379, 41.646071282855, 41.6955792135288, 41.646071282855,
     + 41.6955792135288, 42.3391823122891, 42.2302648648066, 42.1213474173241,
     + 42.081741072785, 42.0520363143807, 42.3094775538848, 42.3985918290977,
     + 42.9629822387798, 42.9431790665102, 42.8144584467582, 43.042194927858,
     + 42.8936711358364, 43.2402266505534, 42.9332774803755, 42.9431790665102,
     + 43.6461916820791, 43.2798329950925, 43.3788488564402, 43.4877663039228,
     + 43.3095377534968, 43.4184552009793, 43.0818012723971, 43.2204234782839,
     + 43.6461916820791, 43.6461916820791, 43.477864717788, 43.4877663039228,
     + 42.9134743081059, 42.9827854110493, 43.2501282366882, 41.9827252114373,
     + 42.2599696232109, 42.6362298963323, 42.6857378270061, 42.7550489299496,
     + 42.428296587502, 42.6560330686018, 42.7581687685824, 42.85732964235,
     + 43.2143087879133, 43.8687705547794, 43.5911201082302, 43.6010361956069,
     + 44.2654140498497, 44.2257497003427, 43.7894418557653, 43.432462710202,
     + 43.6407005451139, 43.4820431470858, 43.6109522829837, 43.432462710202,
     + 43.6208683703604, 43.3531340111879, 43.7001970693745, 43.6010361956069,
     + 43.9183509916632, 43.5613718460999, 43.8588544674026, 43.432462710202,
     + 43.6010361956069, 43.5316235839696, 43.6407005451139, 43.6307844577372,
     + 43.6208683703604, 42.2623643997445, 43.0259031277549, 42.7581687685824,
     + 42.6391757200613, 42.817665292843, 42.6292596326846, 43.2738053121739,
     + 44.1365049139519, 44.6323092827898, 44.0670923023145, 44.1563370887054,
     + 44.29516231198, 44.3546588362405, 44.5529805837757, 45.0388688652369,
     + 45.237190612772, 45.2570227875256, 45.3958480108002, 45.0091206031066,
     + 45.0686171273672, 45.0785332147439, 45.6140019330889, 45.8916523796381,
     + 45.6536662825959, 46.0503097776662, 46.3180441368387, 46.2684636999549,
     + 46.0007293407824, 45.6734984573494, 45.5644214962051, 45.812323680624,
     + 45.7924915058705, 45.8649778315189, 45.4975813864516, 45.4975813864516,
     + 44.9315922683748, 44.3953920512495, 44.7925773972682, 45.4181443172478,
     + 45.0308886048795, 44.4648994868027, 44.1173623090363, 44.6734217934626,
     + 44.2663068137933, 44.554266189657, 44.8521551991711, 44.3358142493466,
     + 43.2733434487464, 43.2733434487464, 42.9158766373295, 43.5414435573091,
     + 43.6605991611147, 44.1967993782401, 44.0875734080849, 44.365603150298,
     + 44.1868697445896, 43.9485585369783, 44.554266189657, 44.6734217934626,
     + 44.9514515356758, 44.6833514271131, 45.1301849413842, 45.358566515345,
     + 45.4876517528011, 45.3387072480441, 45.6663851585096, 46.013922336276,
     + 46.182726108334, 46.4309669495957, 46.1032890391302, 45.8351889305675,
     + 45.7954703959657, 45.7259629604124, 45.7358925940628, 45.4280739508983,
     + 45.2294812778889, 46.2423039102368, 45.9642741680236, 45.7954703959657,
     + 45.5968777229563, 45.3287776143936, 45.7061036931114, 45.8550481978685,
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     >
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     + 15671, 15672, 15673, 15674, 15677, 15678, 15679, 15680, 15681,
     + 15684, 15685, 15686, 15687, 15688, 15691, 15692, 15693, 15694,
     + 15695, 15698, 15700, 15701, 15702, 15705, 15707, 15708, 15709,
     + 15712, 15713, 15714, 15715, 15716, 15719, 15720, 15721, 15722,
     + 15723, 15727, 15728, 15729, 15730, 15733, 15734, 15735, 15736,
     + 15737, 15740, 15741, 15742, 15743, 15744, 15747, 15748, 15749,
     + 15750, 15751, 15755, 15756, 15757, 15758, 15761, 15762, 15763,
     + 15764, 15765, 15768, 15769, 15770, 15771, 15772, 15775, 15776,
     + 15777, 15778, 15779, 15782, 15783, 15784, 15785, 15786, 15789,
     + 15790, 15791, 15792, 15796, 15797, 15798, 15799, 15800, 15803,
     + 15804, 15805, 15806, 15807, 15810, 15811, 15812, 15813, 15814,
     + 15817, 15818, 15819, 15820, 15821, 15824, 15825, 15826, 15827,
     + 15828, 15831, 15832, 15833, 15834, 15835, 15838, 15839, 15840,
     + 15841, 15842, 15845, 15846, 15847, 15848, 15849, 15853, 15854,
     + 15855, 15856, 15859, 15860, 15861, 15862, 15863, 15866, 15867,
     + 15868, 15869, 15870, 15873, 15874, 15875, 15876, 15877, 15880,
     + 15881, 15882, 15883, 15884, 15887, 15888, 15889, 15891, 15894,
     + 15895, 15896, 15897, 15898, 15901, 15902, 15903, 15904, 15905,
     + 15908, 15909, 15910, 15911, 15912, 15915, 15916, 15917, 15918,
     + 15919, 15922, 15923, 15924, 15925, 15926, 15929, 15930, 15931,
     + 15932, 15933, 15936, 15937, 15938, 15939, 15940, 15943, 15944,
     + 15945, 15946, 15947, 15951, 15952, 15953, 15954, 15957, 15958,
     + 15959, 15960, 15961, 15964, 15965, 15966, 15967, 15968, 15971,
     + 15972, 15973, 15974, 15975, 15978, 15979, 15980, 15981, 15982,
     + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995,
     + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008,
     + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021,
     + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034,
     + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048,
     + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059,
     + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo")
     >
     > test_cfit <- function (fast_only=FALSE) {
     + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_)
     + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_)
     +
     + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_))
     + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN))
     + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433))
     + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_))
     +
     + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(-1,1,0)", 1)
     + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_)
     + test("partialAR:::pvmr_par_c(0,0,1)", 0)
     + test("partialAR:::pvmr_par_c(0,1,0)", 1)
     + test("partialAR:::pvmr_par_c(0,1,1)", 2/3)
     + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429)
     + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25)
     + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25)
     +
     + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_)
     + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1)
     + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0)
     + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3)
     +
     + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347)
     +
     + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + }
     >
     >
     > test_lr <- function (fast_only=FALSE) {
     + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference
     + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143)
     + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353)
     +
     + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347)
     +
     + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531)
     + }
     >
     > test.likelihood_ratio.par <- function (fast_only=FALSE) {
     + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184)
     +
     + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347)
     +
     + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE)
     + test("nrow(SAMPLES)", 10)
     + test("sum(SAMPLES$seed)", 55)
     + test("mean(SAMPLES$rw_lrt)", -4.43576369917)
     + test("mean(SAMPLES$mr_lrt)", -3.8960913155)
     + test("mean(SAMPLES$kpss_stat)", 3.7269871366)
     + }
     >
     > test_lr2 <- function(fast_only=FALSE) {
     + test.likelihood_ratio.par(fast_only)
     +
     + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE)
     +
     + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE)
     +
     + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10)
     + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1)
     +
     + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE)
     + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE)
     +
     + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE))
     +
     + print(partialAR:::test.par(data.L))
     + print(partialAR:::test.par(data.L, robust=TRUE))
     +
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW")
     +
     + partialAR:::print.par.lrt(); cat("\n\n")
     + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n")
     +
     + # partialAR:::print.par.lrt.mr(); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n")
     +
     + partialAR:::print.par.lrt.rw(); cat("\n\n")
     + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n")
     +
     + }
     >
     > test_fit.par.both <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.both(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr",
     + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082,
     + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + }
     >
     > test_fit.par.mr <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.mr(data.L)$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.L)$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par",
     + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA,
     + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM)$par",
     + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$stderr",
     + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA,
     + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1))
     +
     + }
     >
     > test_fit.par.rw <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.rw(data.L)$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.L)$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$par",
     + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$stderr",
     + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0))
     + }
     >
     > test_fit.par <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + test("partialAR:::fit.par(data.L, model='ar1')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='ar1')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.L, model='rw')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='rw')$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + }
     >
     > test_fit <- function (fast_only=FALSE) {
     + test("partialAR:::par.rho.cutoff(25)", NA_real_)
     + test("partialAR:::par.rho.cutoff(50)", 0.724)
     + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594)
     + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438)
     +
     + test("partialAR:::estimate.rho.par(numeric())", NA_real_)
     + test("partialAR:::estimate.rho.par(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate.rho.par(x1na)", NA_real_)
     +
     + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN))
     + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433))
     + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     +
     + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0))
     + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3))
     + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429))
     + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25))
     + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25))
     +
     + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_))
     + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0))
     + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1))
     + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3))
     +
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545))
     +
     + test_fit.par.both (fast_only)
     + test_fit.par.mr(fast_only)
     + test_fit.par.rw(fast_only)
     + test_fit.par(fast_only)
     +
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]",
     + structure(list(X = 37.8517816659277, M = 0.00867470536387833,
     + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289),
     + .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") )
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]",
     + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997,
     + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") )
     +
     + print(partialAR:::fit.par(data.L))
     + print(partialAR:::fit.par(data.IBM))
     +
     + test("as.data.frame(partialAR:::fit.par(data.L))",
     + structure(list(robust = FALSE, nu = 5,
     + opt_method = "css",
     + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798,
     + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997,
     + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534,
     + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426,
     + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust",
     + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0",
     + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda",
     + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") )
     + }
     >
     > test_par <- function (fast_only=FALSE) {
     + # Comprehensive unit testing for PAR package
     +
     + options(warn=1)
     +
     + test_cfit(fast_only)
     + test_lr(fast_only)
     + test_fit(fast_only)
     + test_lr2(fast_only)
     +
     + if (all.tests.pass) {
     + cat("SUCCESS! All tests passed.\n")
     + } else {
     + stop("ERRORS! ", all.tests.error.count," tests failed\n")
     + }
     + }
     >
     > test_par(TRUE)
     partialAR:::estimate_rho_par_c(numeric()) -> NA OK
     partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK
     partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK
     partialAR:::estimate_rho_par_c(x1na) -> NA OK
     partialAR:::estimate_par_c(numeric()) -> NA NA NA OK
     partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK
     partialAR:::pvmr_par_c(0,0,0) -> NA OK
     partialAR:::pvmr_par_c(-1,1,0) -> 1 OK
     partialAR:::pvmr_par_c(1,-1,0) -> NA OK
     partialAR:::pvmr_par_c(1,1,-1) -> NA OK
     partialAR:::pvmr_par_c(0,0,1) -> 0 OK
     partialAR:::pvmr_par_c(0,1,0) -> 1 OK
     partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK
     partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK
     partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK
     partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK
     partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK
     partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK
     partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK
     partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK
     partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK
     partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK
     partialAR:::par.rho.cutoff(25) -> NA OK
     partialAR:::par.rho.cutoff(50) -> 0.724 OK
     partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK
     partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK
     partialAR:::estimate.rho.par(numeric()) -> NA OK
     partialAR:::estimate.rho.par(rep(0,5)) -> NA OK
     partialAR:::estimate.rho.par(x1) -> 0.8497954 OK
     partialAR:::estimate.rho.par(x1na) -> NA OK
     partialAR:::estimate.par(numeric()) -> NA NA NA OK
     partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate.par(x1na) -> NA NaN NaN OK
     partialAR:::pvmr.par(0,0,0) -> NaN OK
     partialAR:::pvmr.par(-1,1,0) -> 1 OK
     partialAR:::pvmr.par(1,-1,0) -> NA OK
     partialAR:::pvmr.par(1,1,-1) -> NA OK
     partialAR:::pvmr.par(0,0,1) -> 0 OK
     partialAR:::pvmr.par(0,1,0) -> 1 OK
     partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman.gain.par(0,0,0) -> NA NA OK
     partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK
     partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK
     partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK
     partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK
     partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK
     partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2)
     (0.0494) (0.0306)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2)
     (0.0508)
     M_0 = 0.0000, R_0 = 37.8349
     (NA) (0.3828)
     Proportion of variance attributable to mean reversion (pvmr) = 0.7673
     Negative log likelihood = 238.53
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2)
     (0.0182) (0.1531)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2)
     (0.5998)
     M_0 = 0.0000, R_0 = 177.4729
     (NA) (2.1228)
     Proportion of variance attributable to mean reversion (pvmr) = 0.9493
     Negative log likelihood = 1076.49
     as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 ))
     ERROR: Component "opt_method": 'current' is not a factor
     partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK
     partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK
     nrow(SAMPLES) -> 10 OK
     sum(SAMPLES$seed) -> 55 OK
     mean(SAMPLES$rw_lrt) -> -4.435764 OK
     mean(SAMPLES$mr_lrt) -> -3.896091 OK
     mean(SAMPLES$kpss_stat) -> 3.726987 OK
     partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK
     partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK
     partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK
     partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) :
     Sample size too small (49) to provide accurate p-value
     1 OK
     partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Random Walk -4.45 0.014
     AR(1) -4.45 0.010
     Combined 0.010
    
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
    
     Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1)
     [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Robust RW -2.65 0.071
     Robust AR(1) -2.65 0.010
     Combined 0.060
    
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     PAR OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RRW OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     Warning in regularize.values(x, y, ties, missing(ties)) :
     collapsing to unique 'x' values
     RW OK
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7
     n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4
     n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1
     n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0
     n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0
     n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0
    
    
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
     Robust Model
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8
     n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6
     n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3
     n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1
     n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0
     n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0
    
    
     \begin{table}
     \begin{tabular}{crrr|rrr}
     & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\
     n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\
     n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\
     n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\
     n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\
     n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\
     \end{tabular}
     \caption{Critical Values for Likelihood Ratio Tests}
     \caption*{For each sample size, 40,000 random walks were generated, and then the
     likelihood ratios were calculated under the hypothesis of a random walk
     (left panel) and under the hypothesis of an AR(1) series (right panel).
     For the hypothesis of an AR(1) series, it was found that the critical values
     depend upon the value of $\rho$, and that as $\rho$ increases, the critical values
     for a given quantile decrease. Thus, by using the limiting case of a random walk
     when computing critical values for the AR(1) case, a conservative estimate is
     obtained.}
     \end{table}
    
     Critical Values for Likelihood Ratio Tests
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.7 -2.9 -2.2
     n=100 -4.7 -3.0 -2.2
     n=250 -4.6 -3.0 -2.2
     n=500 -4.7 -3.2 -2.4
     n=1000 -4.8 -3.1 -2.4
     n=2500 -4.8 -3.1 -2.4
    
    
     Critical Values for Likelihood Ratio Tests
     Robust Model
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.5 -2.9 -2.2
     n=100 -4.6 -2.9 -2.2
     n=250 -4.6 -2.9 -2.3
     n=500 -4.6 -3.0 -2.3
     n=1000 -4.5 -3.0 -2.4
     n=2500 -4.7 -3.1 -2.4
    
    
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-release-windows-ix86+x86_64

Version: 1.0.12
Check: tests
Result: ERROR
     Running ‘tests.R’ [11s/11s]
    Running the tests in ‘tests/tests.R’ failed.
    Last 13 lines of output:
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-release-osx-x86_64

Version: 1.0.12
Check: running tests for arch ‘i386’
Result: ERROR
     Running 'tests.R' [15s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     > all.tests.pass <- TRUE
     > all.tests.error.count <- 0
     >
     > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) {
     + # expr is a string representing an R expression, and
     + # out is the output that is expected. Prints and evaluates
     + # expr. If out is given and it matches the output of
     + # evaluating expr, returns TRUE. Otherwise, returns FALSE.
     +
     + cat(expr, "-> ")
     +
     + p <- function (v) {
     + if (length(v) < 5) {
     + cat(v)
     + } else {
     + cat(class(v), "(", length(val), ")")
     + }
     + }
     + p(val)
     +
     + result <- all.equal(val, out, tolerance=tol)
     + if (!isTRUE(result)) {
     + if (!missing(out)) {
     + cat(" (Expecting ")
     + p(out)
     + cat(")")
     + }
     + cat("\nERROR: ", result, "\n")
     + all.tests.pass <<- FALSE
     + all.tests.error.count <<- all.tests.error.count + 1
     + } else {
     + cat(" OK\n")
     + }
     +
     + isTRUE(result)
     + }
     >
     > assert <- function (expr, out) {
     + # expr is astring representing an R expression,
     + # and out is the output that is expected. Prints
     + # and evaluates expr. If out matches the output of
     + # evaluating expr, returns TRUE. Otherwise, stops
     + # the execution with an error message.
     + if (!test(expr, out)) {
     + stop("Expression ", deparse(substitute(expr)),
     + " does not evaluate to its expected value\n")
     + }
     + }
     >
     > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) {
     + R <- R0
     + M <- M0
     + X <- numeric()
     + for (i in 1:length(eps_M)) {
     + M <- rho * M + eps_M[i]
     + R <- R + eps_R[i]
     + X[i] <- M + R
     + }
     + X
     + }
     >
     > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548,
     + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909,
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     >
     > data.IBM <- structure(c(176.668606104443, 175.947896814914, 175.113391321774,
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     + 1L), .Dimnames = list(NULL, "IBM"), index = structure(c(15342,
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     + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995,
     + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008,
     + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021,
     + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034,
     + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048,
     + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059,
     + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo")
     >
     > test_cfit <- function (fast_only=FALSE) {
     + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_)
     + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_)
     +
     + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_))
     + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN))
     + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433))
     + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_))
     +
     + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(-1,1,0)", 1)
     + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_)
     + test("partialAR:::pvmr_par_c(0,0,1)", 0)
     + test("partialAR:::pvmr_par_c(0,1,0)", 1)
     + test("partialAR:::pvmr_par_c(0,1,1)", 2/3)
     + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429)
     + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25)
     + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25)
     +
     + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_)
     + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1)
     + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0)
     + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3)
     +
     + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347)
     +
     + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + }
     >
     >
     > test_lr <- function (fast_only=FALSE) {
     + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference
     + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143)
     + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353)
     +
     + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347)
     +
     + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531)
     + }
     >
     > test.likelihood_ratio.par <- function (fast_only=FALSE) {
     + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184)
     +
     + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347)
     +
     + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE)
     + test("nrow(SAMPLES)", 10)
     + test("sum(SAMPLES$seed)", 55)
     + test("mean(SAMPLES$rw_lrt)", -4.43576369917)
     + test("mean(SAMPLES$mr_lrt)", -3.8960913155)
     + test("mean(SAMPLES$kpss_stat)", 3.7269871366)
     + }
     >
     > test_lr2 <- function(fast_only=FALSE) {
     + test.likelihood_ratio.par(fast_only)
     +
     + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE)
     +
     + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE)
     +
     + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10)
     + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1)
     +
     + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE)
     + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE)
     +
     + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE))
     +
     + print(partialAR:::test.par(data.L))
     + print(partialAR:::test.par(data.L, robust=TRUE))
     +
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW")
     +
     + partialAR:::print.par.lrt(); cat("\n\n")
     + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n")
     +
     + # partialAR:::print.par.lrt.mr(); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n")
     +
     + partialAR:::print.par.lrt.rw(); cat("\n\n")
     + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n")
     +
     + }
     >
     > test_fit.par.both <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.both(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr",
     + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082,
     + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + }
     >
     > test_fit.par.mr <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.mr(data.L)$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.L)$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par",
     + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA,
     + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM)$par",
     + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$stderr",
     + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA,
     + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1))
     +
     + }
     >
     > test_fit.par.rw <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.rw(data.L)$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.L)$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$par",
     + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$stderr",
     + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0))
     + }
     >
     > test_fit.par <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + test("partialAR:::fit.par(data.L, model='ar1')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='ar1')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.L, model='rw')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='rw')$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + }
     >
     > test_fit <- function (fast_only=FALSE) {
     + test("partialAR:::par.rho.cutoff(25)", NA_real_)
     + test("partialAR:::par.rho.cutoff(50)", 0.724)
     + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594)
     + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438)
     +
     + test("partialAR:::estimate.rho.par(numeric())", NA_real_)
     + test("partialAR:::estimate.rho.par(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate.rho.par(x1na)", NA_real_)
     +
     + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN))
     + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433))
     + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     +
     + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0))
     + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3))
     + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429))
     + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25))
     + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25))
     +
     + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_))
     + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0))
     + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1))
     + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3))
     +
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545))
     +
     + test_fit.par.both (fast_only)
     + test_fit.par.mr(fast_only)
     + test_fit.par.rw(fast_only)
     + test_fit.par(fast_only)
     +
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]",
     + structure(list(X = 37.8517816659277, M = 0.00867470536387833,
     + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289),
     + .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") )
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]",
     + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997,
     + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") )
     +
     + print(partialAR:::fit.par(data.L))
     + print(partialAR:::fit.par(data.IBM))
     +
     + test("as.data.frame(partialAR:::fit.par(data.L))",
     + structure(list(robust = FALSE, nu = 5,
     + opt_method = "css",
     + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798,
     + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997,
     + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534,
     + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426,
     + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust",
     + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0",
     + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda",
     + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") )
     + }
     >
     > test_par <- function (fast_only=FALSE) {
     + # Comprehensive unit testing for PAR package
     +
     + options(warn=1)
     +
     + test_cfit(fast_only)
     + test_lr(fast_only)
     + test_fit(fast_only)
     + test_lr2(fast_only)
     +
     + if (all.tests.pass) {
     + cat("SUCCESS! All tests passed.\n")
     + } else {
     + stop("ERRORS! ", all.tests.error.count," tests failed\n")
     + }
     + }
     >
     > test_par(TRUE)
     partialAR:::estimate_rho_par_c(numeric()) -> NA OK
     partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK
     partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK
     partialAR:::estimate_rho_par_c(x1na) -> NA OK
     partialAR:::estimate_par_c(numeric()) -> NA NA NA OK
     partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK
     partialAR:::pvmr_par_c(0,0,0) -> NA OK
     partialAR:::pvmr_par_c(-1,1,0) -> 1 OK
     partialAR:::pvmr_par_c(1,-1,0) -> NA OK
     partialAR:::pvmr_par_c(1,1,-1) -> NA OK
     partialAR:::pvmr_par_c(0,0,1) -> 0 OK
     partialAR:::pvmr_par_c(0,1,0) -> 1 OK
     partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK
     partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK
     partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK
     partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK
     partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK
     partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK
     partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK
     partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK
     partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK
     partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK
     partialAR:::par.rho.cutoff(25) -> NA OK
     partialAR:::par.rho.cutoff(50) -> 0.724 OK
     partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK
     partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK
     partialAR:::estimate.rho.par(numeric()) -> NA OK
     partialAR:::estimate.rho.par(rep(0,5)) -> NA OK
     partialAR:::estimate.rho.par(x1) -> 0.8497954 OK
     partialAR:::estimate.rho.par(x1na) -> NA OK
     partialAR:::estimate.par(numeric()) -> NA NA NA OK
     partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate.par(x1na) -> NA NaN NaN OK
     partialAR:::pvmr.par(0,0,0) -> NaN OK
     partialAR:::pvmr.par(-1,1,0) -> 1 OK
     partialAR:::pvmr.par(1,-1,0) -> NA OK
     partialAR:::pvmr.par(1,1,-1) -> NA OK
     partialAR:::pvmr.par(0,0,1) -> 0 OK
     partialAR:::pvmr.par(0,1,0) -> 1 OK
     partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman.gain.par(0,0,0) -> NA NA OK
     partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK
     partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK
     partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK
     partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK
     partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK
     partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK
     partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420384 OK
     partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2)
     (0.0494) (0.0306)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2)
     (0.0508)
     M_0 = 0.0000, R_0 = 37.8349
     (NA) (0.3828)
     Proportion of variance attributable to mean reversion (pvmr) = 0.7673
     Negative log likelihood = 238.53
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2)
     (0.0182) (0.1531)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2)
     (0.5998)
     M_0 = 0.0000, R_0 = 177.4729
     (NA) (2.1228)
     Proportion of variance attributable to mean reversion (pvmr) = 0.9493
     Negative log likelihood = 1076.49
     as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 ))
     ERROR: Component "opt_method": 'current' is not a factor
     partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK
     partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK
     nrow(SAMPLES) -> 10 OK
     sum(SAMPLES$seed) -> 55 OK
     mean(SAMPLES$rw_lrt) -> -4.435764 OK
     mean(SAMPLES$mr_lrt) -> -3.896091 OK
     mean(SAMPLES$kpss_stat) -> 3.726987 OK
     partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600) < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK
     partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK
     partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK
     partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) :
     Sample size too small (49) to provide accurate p-value
     1 OK
     partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK
    
     Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Random Walk -4.45 0.014
     AR(1) -4.45 0.010
     Combined 0.010
    
    
     Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1)
     [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Robust RW -2.65 0.071
     Robust AR(1) -2.65 0.010
     Combined 0.060
    
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> PAR OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> RRW OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> RW OK
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7
     n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4
     n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1
     n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0
     n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0
     n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0
    
    
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
     Robust Model
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8
     n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6
     n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3
     n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1
     n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0
     n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0
    
    
     \begin{table}
     \begin{tabular}{crrr|rrr}
     & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\
     n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\
     n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\
     n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\
     n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\
     n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\
     \end{tabular}
     \caption{Critical Values for Likelihood Ratio Tests}
     \caption*{For each sample size, 40,000 random walks were generated, and then the
     likelihood ratios were calculated under the hypothesis of a random walk
     (left panel) and under the hypothesis of an AR(1) series (right panel).
     For the hypothesis of an AR(1) series, it was found that the critical values
     depend upon the value of $\rho$, and that as $\rho$ increases, the critical values
     for a given quantile decrease. Thus, by using the limiting case of a random walk
     when computing critical values for the AR(1) case, a conservative estimate is
     obtained.}
     \end{table}
    
     Critical Values for Likelihood Ratio Tests
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.7 -2.9 -2.2
     n=100 -4.7 -3.0 -2.2
     n=250 -4.6 -3.0 -2.2
     n=500 -4.7 -3.2 -2.4
     n=1000 -4.8 -3.1 -2.4
     n=2500 -4.8 -3.1 -2.4
    
    
     Critical Values for Likelihood Ratio Tests
     Robust Model
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.5 -2.9 -2.2
     n=100 -4.6 -2.9 -2.2
     n=250 -4.6 -2.9 -2.3
     n=500 -4.6 -3.0 -2.3
     n=1000 -4.5 -3.0 -2.4
     n=2500 -4.7 -3.1 -2.4
    
    
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64

Version: 1.0.12
Check: running tests for arch ‘x64’
Result: ERROR
     Running 'tests.R' [17s]
    Running the tests in 'tests/tests.R' failed.
    Complete output:
     > all.tests.pass <- TRUE
     > all.tests.error.count <- 0
     >
     > test <- function(expr, out="", val=eval.parent(parse(text=expr), 1), tol=1e-4) {
     + # expr is a string representing an R expression, and
     + # out is the output that is expected. Prints and evaluates
     + # expr. If out is given and it matches the output of
     + # evaluating expr, returns TRUE. Otherwise, returns FALSE.
     +
     + cat(expr, "-> ")
     +
     + p <- function (v) {
     + if (length(v) < 5) {
     + cat(v)
     + } else {
     + cat(class(v), "(", length(val), ")")
     + }
     + }
     + p(val)
     +
     + result <- all.equal(val, out, tolerance=tol)
     + if (!isTRUE(result)) {
     + if (!missing(out)) {
     + cat(" (Expecting ")
     + p(out)
     + cat(")")
     + }
     + cat("\nERROR: ", result, "\n")
     + all.tests.pass <<- FALSE
     + all.tests.error.count <<- all.tests.error.count + 1
     + } else {
     + cat(" OK\n")
     + }
     +
     + isTRUE(result)
     + }
     >
     > assert <- function (expr, out) {
     + # expr is astring representing an R expression,
     + # and out is the output that is expected. Prints
     + # and evaluates expr. If out matches the output of
     + # evaluating expr, returns TRUE. Otherwise, stops
     + # the execution with an error message.
     + if (!test(expr, out)) {
     + stop("Expression ", deparse(substitute(expr)),
     + " does not evaluate to its expected value\n")
     + }
     + }
     >
     > build_par <- function (rho, eps_M, eps_R, R0=0, M0=0) {
     + R <- R0
     + M <- M0
     + X <- numeric()
     + for (i in 1:length(eps_M)) {
     + M <- rho * M + eps_M[i]
     + R <- R + eps_R[i]
     + X[i] <- M + R
     + }
     + X
     + }
     >
     > data.L <- structure(c(37.8517816659277, 37.3893346323175, 37.4385311252548,
     + 37.1138342718688, 37.2319058549183, 37.8616209645152, 37.7238707842909,
     + 37.900978158865, 37.6156384998289, 37.4188525280799, 37.7632279786407,
     + 37.9108174574525, 37.9403353532148, 38.314228699538, 37.8222637701654,
     + 37.5664420068916, 37.3401381393802, 37.0252805845818, 36.7202623283708,
     + 36.7104230297833, 37.2417451535057, 37.3893346323175, 37.9895318461521,
     + 37.7632279786407, 37.7435493814658, 37.8714602631026, 37.5861206040665,
     + 37.487727618192, 37.8025851729905, 37.5369241111293, 36.985923390232,
     + 37.4582097224297, 37.6845135899411, 38.1076034292015, 38.0879248320266,
     + 38.5405325670494, 38.511014671287, 38.6389255529239, 38.7798536105174,
     + 38.5728963231423, 38.6615923034459, 38.3068083822315, 38.2870981643863,
     + 37.6070956487254, 37.6563711933385, 37.7647773914873, 38.0899959859339,
     + 38.0111551145529, 38.7305780659043, 38.4546350160709, 38.9868108978925,
     + 38.9079700265115, 39.1050722049639, 39.1247824228092, 38.7699985015948,
     + 38.2378226197732, 38.6221718677554, 39.2824641655711, 39.1149273138865,
     + 39.0557966603508, 38.8981149175889, 39.2923192744937, 39.7850747206248,
     + 39.4795663440236, 39.1346375317318, 38.9966660068151, 38.4349247982256,
     + 37.8337631539457, 38.2279675108506, 38.8586944818984, 38.346228817922,
     + 38.6813025212912, 39.3415948191068, 39.0755068781961, 38.9769557889698,
     + 39.2627539477259, 39.0459415514282, 39.6569583046307, 40.0511626615356,
     + 40.4552221273631, 40.4158016916726, 40.5340629987441, 40.8888469199585,
     + 40.6720345236608, 40.5439181076667, 40.1792790775297, 40.1300035329166,
     + 40.3172506024464, 40.1694239686071, 40.40594658275, 40.0511626615356,
     + 39.5288418886367, 39.1346375317318, 38.5433309963745, 38.1688368573148,
     + 37.7647773914873, 38.3955043625351, 38.6320269766781, 38.6517371945233,
     + 38.7995638283626, 38.6517371945233, 39.0853619871187, 38.2690477622191,
     + 38.3874972265335, 37.8643454258119, 37.8051206936547, 38.0025364675119,
     + 39.0192277028765, 39.0488400689551, 39.3548345184338, 39.0093569141837,
     + 39.1574187445766, 38.7231040420907, 39.196901899348, 39.9372110513125,
     + 40.183980768634, 40.3419133877198, 40.3813965424912, 39.3252221523552,
     + 39.552250292291, 38.8119411403264, 38.8316827177122, 39.335092941048,
     + 39.621345813141, 40.3813965424912, 40.4801044294198, 40.8058404562842,
     + 40.0655313043197, 39.976694206084, 39.6805705452981, 39.4338008279766,
     + 39.8286323756911, 39.5719918696767, 40.3715257537984, 40.1642391912483,
     + 40.1938515573269, 40.4899752181127, 40.4603628520341, 40.0260481495483,
     + 39.9470818400054, 39.7792784322267, 39.7792784322267, 40.4603628520341,
     + 41.1611888492272, 39.0290984915694, 39.0784524350337, 38.9402613933336,
     + 38.9501321820265, 39.8286323756911, 39.8977278965411, 40.0556605156268,
     + 39.9372110513125, 39.9470818400054, 39.9865649947768, 39.9372110513125,
     + 39.9668234173911, 39.9174694739268, 39.9964357834697, 39.9569526286982,
     + 40.0852728817054, 39.9668234173911, 39.9174694739268, 39.2561266315052,
     + 39.7101829113767, 39.8977278965411, 39.7003121226839, 39.9108897674813,
     + 39.7922544746377, 40.1877054507832, 40.3261132924341, 40.464521134085,
     + 40.464521134085, 40.7116779941759, 40.7413368173868, 41.008266226285,
     + 40.9094034822486, 41.8387132761905, 42.204505429125, 41.8090544529796,
     + 41.5717838672923, 41.2455368119723, 40.9687211286705, 40.9588348542668,
     + 41.0379250494959, 40.7709956405977, 40.4941799572959, 40.8105407382123,
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     >
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     + 186.303359764628, 188.005870885192, 189.640281560934, 189.688924735807,
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     + 178.102712267041, 178.572021126769, 178.210262214062, 179.862620491023,
     + 179.051107254409, 180.048388581333, 178.953334575299, 180.859901817947,
     + 182.443819219531, 186.452499063047, 186.48183086678, 187.889757445966,
     + 188.847929701246, 187.879980178055, 190.089642725945, 189.08258413111,
     + 185.787644845098, 186.736039832467, 185.738758505543, 185.249895109992,
     + 185.983190203318, 182.756691792684, 181.055447176167, 182.228719325489,
     + 180.840347282125, 179.764847811913, 179.999502241778, 177.956053248376,
     + 174.739332105652, 177.281421762516, 180.654579191816, 182.013619431447,
     + 182.805578132239, 180.547029244794, 182.570923702374, 170.935974888267,
     + 169.909361757611, 169.009853109797, 171.072856639021, 171.855038071902,
     + 173.839823457838, 172.910983006292, 173.399846401843, 178.063603195397,
     + 176.137481416927, 175.218418233291, 175.237972769113, 176.254808631859,
     + 173.888709797393, 176.139728143555, 176.935939872984, 176.926110098546,
     + 179.766914910951, 179.953680625262, 180.425509798257, 179.108320023647,
     + 180.071637918511, 181.329849046496, 182.096571452612, 182.037592805988,
     + 180.995636715625, 178.213810549844, 175.893983782621, 174.291730549326,
     + 175.923473105933, 176.621387090987, 174.458836714762, 173.082668293528,
     + 172.748455962656, 173.082668293528, 174.645602429072, 174.439177165887,
     + 174.104964835016, 172.217648143038, 170.418799420996, 169.858502278064,
     + 174.822538368945, 172.768115511531, 175.658069196123, 177.152194910606,
     + 176.955599421859, 179.127979572521, 180.101127241823, 182.194869196986,
     + 181.929465287177, 183.236825287349, 184.377079122086), .Dim = c(502L,
     + 1L), .Dimnames = list(NULL, "IBM"), index = structure(c(15342,
     + 15343, 15344, 15345, 15348, 15349, 15350, 15351, 15352, 15356,
     + 15357, 15358, 15359, 15362, 15363, 15364, 15365, 15366, 15369,
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     + 15723, 15727, 15728, 15729, 15730, 15733, 15734, 15735, 15736,
     + 15737, 15740, 15741, 15742, 15743, 15744, 15747, 15748, 15749,
     + 15750, 15751, 15755, 15756, 15757, 15758, 15761, 15762, 15763,
     + 15764, 15765, 15768, 15769, 15770, 15771, 15772, 15775, 15776,
     + 15777, 15778, 15779, 15782, 15783, 15784, 15785, 15786, 15789,
     + 15790, 15791, 15792, 15796, 15797, 15798, 15799, 15800, 15803,
     + 15804, 15805, 15806, 15807, 15810, 15811, 15812, 15813, 15814,
     + 15817, 15818, 15819, 15820, 15821, 15824, 15825, 15826, 15827,
     + 15828, 15831, 15832, 15833, 15834, 15835, 15838, 15839, 15840,
     + 15841, 15842, 15845, 15846, 15847, 15848, 15849, 15853, 15854,
     + 15855, 15856, 15859, 15860, 15861, 15862, 15863, 15866, 15867,
     + 15868, 15869, 15870, 15873, 15874, 15875, 15876, 15877, 15880,
     + 15881, 15882, 15883, 15884, 15887, 15888, 15889, 15891, 15894,
     + 15895, 15896, 15897, 15898, 15901, 15902, 15903, 15904, 15905,
     + 15908, 15909, 15910, 15911, 15912, 15915, 15916, 15917, 15918,
     + 15919, 15922, 15923, 15924, 15925, 15926, 15929, 15930, 15931,
     + 15932, 15933, 15936, 15937, 15938, 15939, 15940, 15943, 15944,
     + 15945, 15946, 15947, 15951, 15952, 15953, 15954, 15957, 15958,
     + 15959, 15960, 15961, 15964, 15965, 15966, 15967, 15968, 15971,
     + 15972, 15973, 15974, 15975, 15978, 15979, 15980, 15981, 15982,
     + 15985, 15986, 15987, 15988, 15989, 15992, 15993, 15994, 15995,
     + 15996, 15999, 16000, 16001, 16002, 16003, 16006, 16007, 16008,
     + 16009, 16010, 16013, 16014, 16015, 16016, 16017, 16020, 16021,
     + 16022, 16023, 16024, 16027, 16028, 16029, 16030, 16031, 16034,
     + 16035, 16036, 16038, 16041, 16042, 16043, 16044, 16045, 16048,
     + 16049, 16050, 16051, 16052, 16055, 16056, 16057, 16058, 16059,
     + 16062, 16063, 16065, 16066, 16069, 16070), class = "Date"), class = "zoo")
     >
     > test_cfit <- function (fast_only=FALSE) {
     + test("partialAR:::estimate_rho_par_c(numeric())", NA_real_)
     + test("partialAR:::estimate_rho_par_c(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate_rho_par_c(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate_rho_par_c(x1na)", NA_real_)
     +
     + test("partialAR:::estimate_par_c(numeric())", c(NA_real_, NA_real_, NA_real_))
     + test("partialAR:::estimate_par_c(rep(0,5))", c(NaN, NaN, NaN))
     + test("partialAR:::estimate_par_c(x1)", c(0.849795423024, 0, 0.00624752527433))
     + test("partialAR:::estimate_par_c(x1na)", c(NA_real_, NA_real_, NA_real_))
     +
     + test("partialAR:::pvmr_par_c(0,0,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(-1,1,0)", 1)
     + test("partialAR:::pvmr_par_c(1,-1,0)", NA_real_)
     + test("partialAR:::pvmr_par_c(1,1,-1)", NA_real_)
     + test("partialAR:::pvmr_par_c(0,0,1)", 0)
     + test("partialAR:::pvmr_par_c(0,1,0)", 1)
     + test("partialAR:::pvmr_par_c(0,1,1)", 2/3)
     + test("partialAR:::pvmr_par_c(0.5,1,1)", 0.571428571429)
     + test("partialAR:::pvmr_par_c(0.5,1,2)", 0.25)
     + test("partialAR:::pvmr_par_c(0.5,0.5,1)", 0.25)
     +
     + test("partialAR:::kalman_gain_par_mr(0,0,0)", NA_real_)
     + test("partialAR:::kalman_gain_par_mr(0,1,0)", 1)
     + test("partialAR:::kalman_gain_par_mr(0,0,1)", 0)
     + test("partialAR:::kalman_gain_par_mr(0.5,1,1)", 1/3)
     +
     + test("partialAR:::loglik_par_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_c(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik_par_c(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik_par_c(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1076.5235347)
     +
     + test("partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik_par_t_c(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik_par_t_c(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + }
     >
     >
     > test_lr <- function (fast_only=FALSE) {
     + test("partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.kfas(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.kfas(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0)", 1.0439385332) # Note difference
     + test("partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927)", 238.53374143)
     + test("partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1])", 1077.02787353)
     +
     + test("partialAR:::loglik.par.ss(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss(0,0,0,1,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(1,0,0,1,0,0)", 1.4189385332)
     + test("partialAR:::loglik.par.ss(0,0,1,0,0,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0)", 2.75681559961)
     + test("partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1)", 4.25681559961)
     + test("partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0)", 0.918938533205)
     + test("partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1])", 238.533361432)
     + test("partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719)", 1076.5235347)
     +
     + test("partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0)", NA_real_)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(1,0,0,1,0,0)", 1.51558425944)
     + test("partialAR:::loglik.par.ss.t(0,0,1,0,0,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0)", 2.90585876716)
     + test("partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1)", 4.54675277831)
     + test("partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0)", 0.968619589055)
     + test("partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6)", 0.960418255752)
     + test("partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1])", 229.807616531)
     + test("partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1])", 1020.88295106)
     +
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927)", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"css\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"kfas\")", 238.53374143)
     + test("partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method=\"ss\")", 238.533361432)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"sst\")", 229.807616531)
     + test("partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method=\"csst\")", 229.807616531)
     + }
     >
     > test.likelihood_ratio.par <- function (fast_only=FALSE) {
     + test("partialAR:::likelihood_ratio.par(data.L)", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE)", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE)", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='ss')", -4.44824727945)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='ss')", -2.64805301476)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='ss')", -4.44824693057)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='ss')", -2.6480522184)
     +
     + test("partialAR:::likelihood_ratio.par(data.L, opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css')", -4.44824727945)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css')", -2.64805301476)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css')", -4.44824693057)
     + test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css')", -2.6480522184)
     +
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='kfas')", -4.59676088358)
     + if (!fast_only) test("partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='kfas')", -4.5967605347)
     +
     + SAMPLES <- partialAR:::sample.likelihood_ratio.par(nrep=10, use.multicore=FALSE)
     + test("nrow(SAMPLES)", 10)
     + test("sum(SAMPLES$seed)", 55)
     + test("mean(SAMPLES$rw_lrt)", -4.43576369917)
     + test("mean(SAMPLES$mr_lrt)", -3.8960913155)
     + test("mean(SAMPLES$kpss_stat)", 3.7269871366)
     + }
     >
     > test_lr2 <- function(fast_only=FALSE) {
     + test.likelihood_ratio.par(fast_only)
     +
     + test("partialAR:::par.rw.pvalue(-3.5,400) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700) > 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10", TRUE)
     + test("partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05", TRUE)
     +
     + test("partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05", TRUE)
     +
     + test("partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05", TRUE)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50000)", 0.03)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,50)", 0.10)
     + test("partialAR:::par.joint.pvalue(4,-0.5,50)", 1)
     + test("partialAR:::par.joint.pvalue(-4,-0.5,49)", 1)
     +
     + test("partialAR:::test.par.nullrw(data.L)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05", TRUE)
     + test("partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.L)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01", TRUE)
     + test("partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01", TRUE)
     +
     + test("partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10", TRUE)
     + test("partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10", TRUE)
     +
     + test("partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value", TRUE)
     + test("partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value", TRUE)
     + test("partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value", TRUE)
     +
     + test("partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01", c(PAR=TRUE))
     + test("partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10", c(PAR=TRUE))
     + test("partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10", c(PAR=TRUE))
     +
     + print(partialAR:::test.par(data.L))
     + print(partialAR:::test.par(data.L, robust=TRUE))
     +
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L))", "PAR")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE))", "RRW")
     + test("partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM))", "RW")
     +
     + partialAR:::print.par.lrt(); cat("\n\n")
     + partialAR:::print.par.lrt(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt(latex=TRUE); cat("\n\n")
     +
     + # partialAR:::print.par.lrt.mr(); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(robust=TRUE); cat("\n\n")
     + # partialAR:::print.par.lrt.mr(latex=TRUE); cat("\n\n")
     +
     + partialAR:::print.par.lrt.rw(); cat("\n\n")
     + partialAR:::print.par.lrt.rw(robust=TRUE); cat("\n\n")
     + partialAR:::print.par.lrt.rw(latex=TRUE); cat("\n\n")
     +
     + }
     >
     > test_fit.par.both <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.both(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='ss')$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.L, opt_method='kfas')$stderr",
     + structure(c(0.0480869790579741, 0.0299959210912542, 0.0482633848885082,
     + NA, 0.366440477748884), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.both(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + }
     >
     > test_fit.par.mr <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.mr(data.L)$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.L)$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='ss')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$par",
     + structure(c(1, 0.392621113047498, 0, 0, 37.8517816705312), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.L, opt_method='kfas')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901654, NA, NA,
     + 0.392621124727183), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM)$par",
     + structure(c(0.989394562548544, 2.06766254187052, 0, 0, 177.378135957708
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$stderr",
     + structure(c(0.00711953959492437, 0.0652545415824236, NA, NA,
     + 2.18393834163026), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0.996850903105148, 1.47881632988678, 0, 0, 176.742922370692
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.996784426974733, 1.33994364448777, 0, 0, 176.717640850721
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 2.10195614607977, 0, 0, 183.429724544732), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.mr(data.IBM)$pvmr", c(pvmr=1))
     +
     + }
     >
     > test_fit.par.rw <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par.rw(data.L)$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.L)$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='ss')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.L, opt_method='kfas')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$par",
     + structure(c(0, 0, 2.07281796275108, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$stderr",
     + structure(c(NA, NA, 0.0925143932669985, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + if (!fast_only) test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, opt_method='ss')$par",
     + structure(c(0, 0, 1.47924935869178, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0, 0, 1.34077692991459, 0, 176.668606104443), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par.rw(data.IBM)$pvmr", c(pvmr=0))
     + }
     >
     > test_fit.par <- function (fast_only=FALSE) {
     + test("partialAR:::fit.par(data.L)$par",
     + structure(c(0.871991364792238, 0.338198849510798, 0.192519577779812,
     + 0, 37.8348806008997), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.L)$stderr",
     + structure(c(0.0493755130952366, 0.0306037545403534, 0.0507506043059735,
     + NA, 0.382843915239426), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + if (!fast_only) test("partialAR:::fit.par(data.L, opt_method='kfas')$par",
     + structure(c(0.873239025413773, 0.334187559078876, 0.187013759524079,
     + 0, 37.8228485852872), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$par",
     + structure(c(0.976388651908034, 2.01216604959705, 0.467711046901045,
     + 0, 177.472892129038), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM)$stderr",
     + structure(c(0.018222371388718, 0.153130468131214, 0.599803359236283,
     + NA, 2.12284254607983), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE)$par",
     + structure(c(0.982921831279379, 1.30721045019958, 0.690103593777354,
     + 0, 176.743925850553), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par",
     + structure(c(0.985936838750558, 1.20382984003629, 0.587584874718192,
     + 0, 176.716597228655), .Names = c("rho", "sigma_M", "sigma_R",
     + "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, rho.max=0.95)$par",
     + structure(c(0.95, 1.8101310703133, 0.998701976498605, 0, 176.958377474755
     + ), .Names = c("rho", "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.IBM, lambda=2)$pvmr", c(pvmr=1))
     + test("partialAR:::fit.par(data.IBM, lambda=-2)$pvmr", c(pvmr=0.0442039289027))
     + test("partialAR:::fit.par(data.L, model='ar1')$par",
     + structure(c(1, 0.392621113046972, 0, 0, 37.8517816705337), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='ar1')$stderr",
     + structure(c(1.55086108092093e-05, 0.0123907243901383, NA, NA,
     + 0.392621124942204), .Names = c("rho.se", "sigma_M.se", "sigma_R.se",
     + "M0.se", "R0.se")) )
     + test("partialAR:::fit.par(data.L, model='rw')$par",
     + structure(c(0, 0, 0.392609091324016, 0, 37.8517816659277), .Names = c("rho",
     + "sigma_M", "sigma_R", "M0", "R0")) )
     + test("partialAR:::fit.par(data.L, model='rw')$stderr",
     + structure(c(NA, NA, 0.0175230013091655, NA, 0), .Names = c("rho.se",
     + "sigma_M.se", "sigma_R.se", "M0.se", "R0.se")) )
     + }
     >
     > test_fit <- function (fast_only=FALSE) {
     + test("partialAR:::par.rho.cutoff(25)", NA_real_)
     + test("partialAR:::par.rho.cutoff(50)", 0.724)
     + test("partialAR:::par.rho.cutoff(50,0.01)", 0.594)
     + test("partialAR:::par.rho.cutoff(50,.00001)", 0.438)
     +
     + test("partialAR:::estimate.rho.par(numeric())", NA_real_)
     + test("partialAR:::estimate.rho.par(rep(0,5))", NaN)
     + x1 <- build_par(0.95, rep(0,10), rep(0,10), M0=1)
     + test("partialAR:::estimate.rho.par(x1)", 0.8497954230236)
     + x1na <- x1
     + x1na[1] <- NA
     + test("partialAR:::estimate.rho.par(x1na)", NA_real_)
     +
     + test("partialAR:::estimate.par(numeric())", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     + test("partialAR:::estimate.par(rep(0,5))", c(rho=NaN, sigma_M=NaN, sigma_R=NaN))
     + test("partialAR:::estimate.par(x1)", c(rho=0.849795423024, sigma_M=0, sigma_R=0.00624752527433))
     + test("partialAR:::estimate.par(x1na)", c(rho=NA_real_, sigma_M=NA_real_, sigma_R=NA_real_))
     +
     + test("partialAR:::pvmr.par(0,0,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(-1,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(1,-1,0)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(1,1,-1)", c(pvmr=NA_real_))
     + test("partialAR:::pvmr.par(0,0,1)", c(pvmr=0))
     + test("partialAR:::pvmr.par(0,1,0)", c(pvmr=1))
     + test("partialAR:::pvmr.par(0,1,1)", c(pvmr=2/3))
     + test("partialAR:::pvmr.par(0.5,1,1)", c(pvmr=0.571428571429))
     + test("partialAR:::pvmr.par(0.5,1,2)", c(pvmr=0.25))
     + test("partialAR:::pvmr.par(0.5,0.5,1)", c(pvmr=0.25))
     +
     + test("partialAR:::kalman.gain.par(0,0,0)", c(NA_real_, NA_real_))
     + test("partialAR:::kalman.gain.par(0,1,0)", c(1,0))
     + test("partialAR:::kalman.gain.par(0,0,1)", c(0,1))
     + test("partialAR:::kalman.gain.par(0.5,1,1)", c(1/3,2/3))
     +
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(1,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,1)", c(1,0))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0,0)", c(0,1))
     + test("partialAR:::kalman.gain.from.pvmr(0.8,0.8)", c(0.545454545455, 0.454545454545))
     +
     + test_fit.par.both (fast_only)
     + test_fit.par.mr(fast_only)
     + test_fit.par.rw(fast_only)
     + test_fit.par(fast_only)
     +
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,]",
     + structure(list(X = 37.8517816659277, M = 0.00867470536387833,
     + R = 37.8431069605638, eps_M = 0.00867470536387833, eps_R = 0.00822635966417289),
     + .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 1L, class = "data.frame") )
     + test("partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),]",
     + structure(list(X = 48.0305776082708, M = 0.379272544771068, R = 47.6513050634997,
     + eps_M = 0.159638785630931, eps_R = 0.151387973638877), .Names = c("X",
     + "M", "R", "eps_M", "eps_R"), row.names = 502L, class = "data.frame") )
     +
     + print(partialAR:::fit.par(data.L))
     + print(partialAR:::fit.par(data.IBM))
     +
     + test("as.data.frame(partialAR:::fit.par(data.L))",
     + structure(list(robust = FALSE, nu = 5,
     + opt_method = "css",
     + n = 502L, rho = 0.871991364792238, sigma_M = 0.338198849510798,
     + sigma_R = 0.192519577779812, M0 = 0, R0 = 37.8348806008997,
     + rho.se = 0.0493755130952366, sigma_M.se = 0.0306037545403534,
     + sigma_R.se = 0.0507506043059735, M0.se = NA_real_, R0.se = 0.382843915239426,
     + lambda = 0, pvmr = 0.767280179062111, negloglik = 238.531977143138), .Names = c("robust",
     + "nu", "opt_method", "n", "rho", "sigma_M", "sigma_R", "M0", "R0",
     + "rho.se", "sigma_M.se", "sigma_R.se", "M0.se", "R0.se", "lambda",
     + "pvmr", "negloglik"), row.names = c(NA, -1L), class = "data.frame") )
     + }
     >
     > test_par <- function (fast_only=FALSE) {
     + # Comprehensive unit testing for PAR package
     +
     + options(warn=1)
     +
     + test_cfit(fast_only)
     + test_lr(fast_only)
     + test_fit(fast_only)
     + test_lr2(fast_only)
     +
     + if (all.tests.pass) {
     + cat("SUCCESS! All tests passed.\n")
     + } else {
     + stop("ERRORS! ", all.tests.error.count," tests failed\n")
     + }
     + }
     >
     > test_par(TRUE)
     partialAR:::estimate_rho_par_c(numeric()) -> NA OK
     partialAR:::estimate_rho_par_c(rep(0,5)) -> NA OK
     partialAR:::estimate_rho_par_c(x1) -> 0.8497954 OK
     partialAR:::estimate_rho_par_c(x1na) -> NA OK
     partialAR:::estimate_par_c(numeric()) -> NA NA NA OK
     partialAR:::estimate_par_c(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate_par_c(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate_par_c(x1na) -> NA NaN NaN OK
     partialAR:::pvmr_par_c(0,0,0) -> NA OK
     partialAR:::pvmr_par_c(-1,1,0) -> 1 OK
     partialAR:::pvmr_par_c(1,-1,0) -> NA OK
     partialAR:::pvmr_par_c(1,1,-1) -> NA OK
     partialAR:::pvmr_par_c(0,0,1) -> 0 OK
     partialAR:::pvmr_par_c(0,1,0) -> 1 OK
     partialAR:::pvmr_par_c(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr_par_c(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr_par_c(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr_par_c(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman_gain_par_mr(0,0,0) -> NA OK
     partialAR:::kalman_gain_par_mr(0,1,0) -> 1 OK
     partialAR:::kalman_gain_par_mr(0,0,1) -> 0 OK
     partialAR:::kalman_gain_par_mr(0.5,1,1) -> 0.3333333 OK
     partialAR:::loglik_par_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_c(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik_par_c(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik_par_c(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik_par_c(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik_par_c(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik_par_c(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1076.524 OK
     partialAR:::loglik_par_t_c(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik_par_t_c(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik_par_t_c(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik_par_t_c(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik_par_t_c(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik_par_t_c(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik_par_t_c(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par.kfas(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.kfas(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.kfas(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.kfas(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.kfas(0.5,0.5,1,0,1,0) -> 1.043939 OK
     partialAR:::loglik.par.kfas(data.L, 0.8720, 0.3385, 0.1927) -> 238.5337 OK
     partialAR:::loglik.par.kfas(data.IBM, 0.9764, 2.0136, 0.4719, 0, data.IBM[1]) -> 1077.028 OK
     partialAR:::loglik.par.ss(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss(0,0,0,1,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,0,1,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(1,0,0,1,0,0) -> 1.418939 OK
     partialAR:::loglik.par.ss(0,0,1,0,0,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(c(0,0,0),0,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,0,0),0.5,1,0,0,0) -> 2.756816 OK
     partialAR:::loglik.par.ss(c(0,1,2),0,0,1,0,1) -> 4.256816 OK
     partialAR:::loglik.par.ss(0.5,0.5,1,0,1,0) -> 0.9189385 OK
     partialAR:::loglik.par.ss(data.L, 0.8720, 0.3385, 0.1927, 0, data.L[1]) -> 238.5334 OK
     partialAR:::loglik.par.ss(data.IBM, 0.9764, 2.0136, 0.4719) -> 1076.524 OK
     partialAR:::loglik.par.ss.t(numeric(),0,0,1,0,0) -> NA OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,0,1,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(1,0,0,1,0,0) -> 1.515584 OK
     partialAR:::loglik.par.ss.t(0,0,1,0,0,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,0,0),0.5,1,0,0,0) -> 2.905859 OK
     partialAR:::loglik.par.ss.t(c(0,1,2),0,0,1,0,1) -> 4.546753 OK
     partialAR:::loglik.par.ss.t(0.5,0.5,1,0,1,0) -> 0.9686196 OK
     partialAR:::loglik.par.ss.t(0,0,0,1,0,0,6) -> 0.9604183 OK
     partialAR:::loglik.par.ss.t(data.L, 0.8958, 0.2612, 0.1768, 0, data.L[1]) -> 229.8076 OK
     partialAR:::loglik.par.ss.t(data.IBM, 0.9829, 1.3072, 0.6901, 0, data.IBM[1]) -> 1020.883 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927) -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="css") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="kfas") -> 238.5337 OK
     partialAR:::loglik.par(data.L, 0.8720, 0.3385, 0.1927, calc_method="ss") -> 238.5334 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="sst") -> 229.8076 OK
     partialAR:::loglik.par(data.L, 0.8958, 0.2612, 0.1768, calc_method="csst") -> 229.8076 OK
     partialAR:::par.rho.cutoff(25) -> NA OK
     partialAR:::par.rho.cutoff(50) -> 0.724 OK
     partialAR:::par.rho.cutoff(50,0.01) -> 0.594 OK
     partialAR:::par.rho.cutoff(50,.00001) -> 0.438 OK
     partialAR:::estimate.rho.par(numeric()) -> NA OK
     partialAR:::estimate.rho.par(rep(0,5)) -> NA OK
     partialAR:::estimate.rho.par(x1) -> 0.8497954 OK
     partialAR:::estimate.rho.par(x1na) -> NA OK
     partialAR:::estimate.par(numeric()) -> NA NA NA OK
     partialAR:::estimate.par(rep(0,5)) -> NA NaN NaN OK
     partialAR:::estimate.par(x1) -> 0.8497954 0 0.006247525 OK
     partialAR:::estimate.par(x1na) -> NA NaN NaN OK
     partialAR:::pvmr.par(0,0,0) -> NaN OK
     partialAR:::pvmr.par(-1,1,0) -> 1 OK
     partialAR:::pvmr.par(1,-1,0) -> NA OK
     partialAR:::pvmr.par(1,1,-1) -> NA OK
     partialAR:::pvmr.par(0,0,1) -> 0 OK
     partialAR:::pvmr.par(0,1,0) -> 1 OK
     partialAR:::pvmr.par(0,1,1) -> 0.6666667 OK
     partialAR:::pvmr.par(0.5,1,1) -> 0.5714286 OK
     partialAR:::pvmr.par(0.5,1,2) -> 0.25 OK
     partialAR:::pvmr.par(0.5,0.5,1) -> 0.25 OK
     partialAR:::kalman.gain.par(0,0,0) -> NA NA OK
     partialAR:::kalman.gain.par(0,1,0) -> 1 0 OK
     partialAR:::kalman.gain.par(0,0,1) -> 0 1 OK
     partialAR:::kalman.gain.par(0.5,1,1) -> 0.3333333 0.6666667 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(1,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,1) -> 1 0 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0,0) -> 0 1 OK
     partialAR:::kalman.gain.from.pvmr(0.8,0.8) -> 0.5454545 0.4545455 OK
     partialAR:::fit.par.both(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.both(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par.both(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par.mr(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.mr(data.IBM)$pvmr -> 1 OK
     partialAR:::fit.par.rw(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par.rw(data.IBM)$pvmr -> 0 OK
     partialAR:::fit.par(data.L)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM)$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, robust=TRUE, nu=3)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, rho.max=0.95)$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.IBM, lambda=2)$pvmr -> 1 OK
     partialAR:::fit.par(data.IBM, lambda=-2)$pvmr -> 0.04420393 OK
     partialAR:::fit.par(data.L, model='ar1')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='ar1')$stderr -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$par -> numeric ( 5 ) OK
     partialAR:::fit.par(data.L, model='rw')$stderr -> numeric ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[1,] -> data.frame ( 5 ) OK
     partialAR:::statehistory.par(partialAR:::fit.par(data.L))[length(data.L),] -> data.frame ( 5 ) OK
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.8720 M[t-1] + eps_M,t, eps_M,t ~ N(0, 0.3382^2)
     (0.0494) (0.0306)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.1925^2)
     (0.0508)
     M_0 = 0.0000, R_0 = 37.8349
     (NA) (0.3828)
     Proportion of variance attributable to mean reversion (pvmr) = 0.7673
     Negative log likelihood = 238.53
     Fitted model:
     X[t] = M[t] + R[t]
     M[t] = 0.9764 M[t-1] + eps_M,t, eps_M,t ~ N(0, 2.0122^2)
     (0.0182) (0.1531)
     R[t] = R[t-1] + eps_R,t, eps_R,t ~ N(0, 0.4677^2)
     (0.5998)
     M_0 = 0.0000, R_0 = 177.4729
     (NA) (2.1228)
     Proportion of variance attributable to mean reversion (pvmr) = 0.9493
     Negative log likelihood = 1076.49
     as.data.frame(partialAR:::fit.par(data.L)) -> data.frame ( 17 ) (Expecting data.frame ( 17 ))
     ERROR: Component "opt_method": 'current' is not a factor
     partialAR:::likelihood_ratio.par(data.L) -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE) -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE) -> -2.648052 OK
     partialAR:::likelihood_ratio.par(data.L, opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='rw', robust=TRUE, opt_method='css') -> -2.648053 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', opt_method='css') -> -4.448247 OK
     partialAR:::likelihood_ratio.par(data.L, null_model='ar1', robust=TRUE, opt_method='css') -> -2.648052 OK
     nrow(SAMPLES) -> 10 OK
     sum(SAMPLES$seed) -> 55 OK
     mean(SAMPLES$rw_lrt) -> -4.435764 OK
     mean(SAMPLES$mr_lrt) -> -3.896091 OK
     mean(SAMPLES$kpss_stat) -> 3.726987 OK
     partialAR:::par.rw.pvalue(-3.5,400) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600) < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700) > 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-3.5,400, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.rw.pvalue(-1,500, robust=TRUE) > 0.10 -> TRUE OK
     partialAR:::par.mr.pvalue(-1,600, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.1, 700, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,400,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 500,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-2,600, robust=TRUE,ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.mr.pvalue(-0.5, 700, robust=TRUE,ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,500) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-1,-0.25,500) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-0.8,500, robust=TRUE) < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-0.1,500, robust=TRUE) > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-5,-2,500, ar1test='kpss') < 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-3,-1,500, ar1test='kpss') > 0.05 -> TRUE OK
     partialAR:::par.joint.pvalue(-4,-0.5,50000) -> 0.03 OK
     partialAR:::par.joint.pvalue(-4,-0.5,50) -> 0.1 OK
     partialAR:::par.joint.pvalue(4,-0.5,50) -> 1 OK
     partialAR:::par.joint.pvalue(-4,-0.5,49) -> Warning in partialAR:::par.joint.pvalue(-4, -0.5, 49) :
     Sample size too small (49) to provide accurate p-value
     1 OK
     partialAR:::test.par.nullrw(data.L)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM)$p.value > 0.05 -> TRUE OK
     partialAR:::test.par.nullrw(data.L, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullrw(data.IBM, robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.L)$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE)$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.L, robust=TRUE, ar1test='kpss')$p.value <= 0.01 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM)$p.value < 0.05 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, robust=TRUE)$p.value < 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss')$p.value > 0.10 -> TRUE OK
     partialAR:::test.par.nullmr(data.IBM, ar1test='kpss', robust=TRUE)$p.value > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='rw')$p.value == partialAR:::test.par.nullrw(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.L)$p.value -> TRUE OK
     partialAR:::test.par(data.IBM, null_hyp='mr')$p.value == partialAR:::test.par.nullmr(data.IBM)$p.value -> TRUE OK
     partialAR:::test.par(data.L)$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM)$p.value['PAR'] > 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, robust=TRUE)$p.value['PAR'] > 0.10 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss')$p.value['PAR'] <= 0.01 -> TRUE OK
     partialAR:::test.par(data.L, ar1test='kpss',robust=TRUE)$p.value['PAR'] <= 0.10 -> TRUE OK
     partialAR:::test.par(data.IBM, ar1test='kpss')$p.value['PAR'] > 0.10 -> TRUE OK
    
     Test of [Random Walk or AR(1)] vs Almost AR(1) [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Random Walk -4.45 0.014
     AR(1) -4.45 0.010
     Combined 0.010
    
    
     Test of [Robust Random Walk or Robust AR(1)] vs Robust Almost AR(1)
     [LR test for AR1]
    
     data: data.L
    
     Hypothesis Statistic p-value
     Robust RW -2.65 0.071
     Robust AR(1) -2.65 0.010
     Combined 0.060
    
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L)) -> PAR OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.L, robust=TRUE)) -> RRW OK
     partialAR:::which.hypothesis.partest(partialAR:::test.par(data.IBM)) -> RW OK
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.7 -2.9 -2.2 | -2.6 -1.2 -0.7
     n=100 -4.7 -3.0 -2.2 | -2.4 -1.0 -0.4
     n=250 -4.6 -3.0 -2.2 | -1.9 -0.5 -0.1
     n=500 -4.7 -3.2 -2.4 | -1.6 -0.3 -0.0
     n=1000 -4.8 -3.1 -2.4 | -1.4 -0.1 -0.0
     n=2500 -4.8 -3.1 -2.4 | -1.3 -0.0 -0.0
    
    
     Critical Values for Likelihood Ratio Tests
     Single Hypothesis Test
     Robust Model
    
     NULL: Random Walk | NULL: AR(1)
     p=0.01 p=0.05 p=0.10 | p=0.01 p=0.05 p=0.10
     ------------------------------------------------------------
     n=50 -4.5 -2.9 -2.2 | -2.9 -1.4 -0.8
     n=100 -4.6 -2.9 -2.2 | -2.8 -1.2 -0.6
     n=250 -4.6 -2.9 -2.3 | -2.2 -0.8 -0.3
     n=500 -4.6 -3.0 -2.3 | -1.9 -0.6 -0.1
     n=1000 -4.5 -3.0 -2.4 | -1.6 -0.3 -0.0
     n=2500 -4.7 -3.1 -2.4 | -1.3 -0.2 -0.0
    
    
     \begin{table}
     \begin{tabular}{crrr|rrr}
     & \multicolumn{3}{c}{NULL: Random Walk} & \multicolumn{3}{c}{NULL: AR(1)} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10} & p=0.01 & p=0.05 & p=0.10\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 & -2.6 & -1.2 & -0.7 \\
     n=100 & -4.7 & -3.0 & -2.2 & -2.4 & -1.0 & -0.4 \\
     n=250 & -4.6 & -3.0 & -2.2 & -1.9 & -0.5 & -0.1 \\
     n=500 & -4.7 & -3.2 & -2.4 & -1.6 & -0.3 & -0.0 \\
     n=1000 & -4.8 & -3.1 & -2.4 & -1.4 & -0.1 & -0.0 \\
     n=2500 & -4.8 & -3.1 & -2.4 & -1.3 & -0.0 & -0.0 \\
     \end{tabular}
     \caption{Critical Values for Likelihood Ratio Tests}
     \caption*{For each sample size, 40,000 random walks were generated, and then the
     likelihood ratios were calculated under the hypothesis of a random walk
     (left panel) and under the hypothesis of an AR(1) series (right panel).
     For the hypothesis of an AR(1) series, it was found that the critical values
     depend upon the value of $\rho$, and that as $\rho$ increases, the critical values
     for a given quantile decrease. Thus, by using the limiting case of a random walk
     when computing critical values for the AR(1) case, a conservative estimate is
     obtained.}
     \end{table}
    
     Critical Values for Likelihood Ratio Tests
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.7 -2.9 -2.2
     n=100 -4.7 -3.0 -2.2
     n=250 -4.6 -3.0 -2.2
     n=500 -4.7 -3.2 -2.4
     n=1000 -4.8 -3.1 -2.4
     n=2500 -4.8 -3.1 -2.4
    
    
     Critical Values for Likelihood Ratio Tests
     Robust Model
     Null hypothesis: Random Walk
    
     p=0.01 p=0.05 p=0.10
     ----------------------------
     n=50 -4.5 -2.9 -2.2
     n=100 -4.6 -2.9 -2.2
     n=250 -4.6 -2.9 -2.3
     n=500 -4.6 -3.0 -2.3
     n=1000 -4.5 -3.0 -2.4
     n=2500 -4.7 -3.1 -2.4
    
    
     \begin{tabular}{crrr}
     & \multicolumn{3}{c}{NULL: Random Walk} \\
     & \multicolumn{1}{c}{p=0.01} & \multicolumn{1}{c}{p=0.05} & \multicolumn{1}{c}{p=0.10}\\
     \hline
     n=50 & -4.7 & -2.9 & -2.2 \\
     n=100 & -4.7 & -3.0 & -2.2 \\
     n=250 & -4.6 & -3.0 & -2.2 \\
     n=500 & -4.7 & -3.2 & -2.4 \\
     n=1000 & -4.8 & -3.1 & -2.4 \\
     n=2500 & -4.8 & -3.1 & -2.4 \\
     \end{tabular}
    
    
     Error in test_par(TRUE) : ERRORS! 1 tests failed
     Execution halted
Flavor: r-oldrel-windows-ix86+x86_64