saeMSPE: Compute MSPE Estimates for the Fay Herriot Model and Nested Error Regression Model

We describe a new R package entitled 'saeMSPE' for the well-known Fay Herriot model and nested error regression model in small area estimation. Based on this package, it is possible to easily compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for these two models.

Version: 1.0
Depends: Matrix
Imports: Rcpp (≥ 1.0.7), smallarea
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-03-29
Author: Peiwen Xiao [aut, cre], Xiaohui Liu [aut], Yuzi Liu [aut], Shaochu Liu [aut], Jiming Jiang [ths]
Maintainer: Peiwen Xiao <2569613200 at qq.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: saeMSPE results

Documentation:

Reference manual: saeMSPE.pdf

Downloads:

Package source: saeMSPE_1.0.tar.gz
Windows binaries: r-devel: saeMSPE_1.0.zip, r-release: saeMSPE_1.0.zip, r-oldrel: saeMSPE_1.0.zip
macOS binaries: r-release (arm64): saeMSPE_1.0.tgz, r-release (x86_64): saeMSPE_1.0.tgz, r-oldrel: saeMSPE_1.0.tgz

Linking:

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