Package: saeMSPE 1.3

saeMSPE: Computing MSPE Estimates in Small Area Estimation

Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation.

Authors:Peiwen Xiao [aut, cre], Xiaohui Liu [aut], Yu Zhang [aut], Yuzi Liu [aut], Jiming Jiang [ths]

saeMSPE_1.3.tar.gz
saeMSPE_1.3.zip(r-4.5)saeMSPE_1.3.zip(r-4.4)saeMSPE_1.3.zip(r-4.3)
saeMSPE_1.3.tgz(r-4.4-x86_64)saeMSPE_1.3.tgz(r-4.4-arm64)saeMSPE_1.3.tgz(r-4.3-x86_64)saeMSPE_1.3.tgz(r-4.3-arm64)
saeMSPE_1.3.tar.gz(r-4.5-noble)saeMSPE_1.3.tar.gz(r-4.4-noble)
saeMSPE_1.2.tgz(r-4.4-emscripten)saeMSPE_1.3.tgz(r-4.3-emscripten)
saeMSPE.pdf |saeMSPE.html✨
saeMSPE/json (API)

# Install 'saeMSPE' in R:
install.packages('saeMSPE', repos = c('https://exce1sior1008.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • wheatarea - Wheat area measurement and satellite data.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.08 score 12 scripts 164 downloads 19 exports 6 dependencies

Last updated 5 days agofrom:0a8a6bd90b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-win-x86_64OKNov 19 2024
R-4.5-linux-x86_64OKNov 19 2024
R-4.4-win-x86_64OKNov 19 2024
R-4.4-mac-x86_64OKNov 19 2024
R-4.4-mac-aarch64OKNov 19 2024
R-4.3-win-x86_64OKNov 19 2024
R-4.3-mac-x86_64OKNov 19 2024
R-4.3-mac-aarch64OKNov 19 2024

Exports:mspeFHdbmspeFHDLmspeFHDRSmspeFHjackmspeFHlinmspeFHMPRmspeFHpbmspeFHPRmspeFHsumcamspeNERdbmspeNERDLmspeNERjackmspeNERlinmspeNERpbmspeNERPRmspeNERsumcavarfhvarnervarOBP

Dependencies:latticeMASSMatrixRcppRcppArmadillosmallarea