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: 1.2
Depends: R (≥ 3.5.0), Matrix, smallarea
Imports: Rcpp (≥ 1.0.7)
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-10-21
DOI: 10.32614/CRAN.package.saeMSPE
Author: Peiwen Xiao [aut, cre], Xiaohui Liu [aut], Yuzi Liu [aut], Shaochu Liu [aut], Jiming Jiang [ths]
Maintainer: Peiwen Xiao <2569613200 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: saeMSPE results


Reference manual: saeMSPE.pdf


Package source: saeMSPE_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): saeMSPE_1.2.tgz, r-oldrel (arm64): saeMSPE_1.2.tgz, r-release (x86_64): saeMSPE_1.2.tgz, r-oldrel (x86_64): saeMSPE_1.2.tgz
Old sources: saeMSPE archive


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