## generalCorr: Generalized Correlations and Various Causal Paths

Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() gives easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X,Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y and causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3 for the causal path X to Y requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns is easiest to use. Several functions whose names begin with 'boot' provide bootstrap statistical inference including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. See five vignettes of the package for theory and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

 Version: 1.1.9 Depends: R (≥ 3.0.0), np (≥ 0.60), xtable (≥ 1.8), meboot (≥ 1.4), psych, lattice Suggests: R.rsp Published: 2021-10-18 Author: Prof. H. D. Vinod, Fordham University, NY. Maintainer: H. D. Vinod License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: no Materials: NEWS CRAN checks: generalCorr results

#### Documentation:

 Reference manual: generalCorr.pdf Vignettes: generalCorr-vignette generalCorr-vignette2 generalCorr-vignette3 generalCorr-vignette4 generalCorr-vignette5