Package: saeMSPE 1.2
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.
Authors:
saeMSPE_1.2.tar.gz
saeMSPE_1.2.zip(r-4.5)saeMSPE_1.2.zip(r-4.4)saeMSPE_1.2.zip(r-4.3)
saeMSPE_1.2.tgz(r-4.4-x86_64)saeMSPE_1.2.tgz(r-4.4-arm64)saeMSPE_1.2.tgz(r-4.3-x86_64)saeMSPE_1.2.tgz(r-4.3-arm64)
saeMSPE_1.2.tar.gz(r-4.5-noble)saeMSPE_1.2.tar.gz(r-4.4-noble)
saeMSPE_1.2.tgz(r-4.4-emscripten)saeMSPE_1.2.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')) |
- wheatarea - Wheat area measurement and satellite data.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:5cfdaf38d2. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 23 2024 |
| R-4.5-win-x86_64 | NOTE | Oct 23 2024 |
| R-4.5-linux-x86_64 | NOTE | Oct 23 2024 |
| R-4.4-win-x86_64 | NOTE | Oct 23 2024 |
| R-4.4-mac-x86_64 | NOTE | Oct 23 2024 |
| R-4.4-mac-aarch64 | NOTE | Oct 23 2024 |
| R-4.3-win-x86_64 | NOTE | Oct 23 2024 |
| R-4.3-mac-x86_64 | NOTE | Oct 23 2024 |
| R-4.3-mac-aarch64 | NOTE | Oct 23 2024 |
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