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 qq.com> |
| 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: saeMSPE_1.2.zip, r-release: saeMSPE_1.2.zip, r-oldrel: saeMSPE_1.2.zip |
| 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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