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Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.
| Version: | 0.1.2 |
| Imports: | stats, utils, magic, MASS, corpcor, progress, fpc |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2023-10-15 |
| DOI: | 10.32614/CRAN.package.sae.prop |
| Author: | M. Rijalus Sholihin [aut, cre], Cucu Sumarni [aut] |
| Maintainer: | M. Rijalus Sholihin <m.rijalussholihin at bps.go.id> |
| BugReports: | https://github.com/mrijalussholihin/sae.prop/issues |
| License: | GPL-3 |
| URL: | https://github.com/mrijalussholihin/sae.prop |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | sae.prop results |
| Reference manual: | sae.prop.pdf |
| Package source: | sae.prop_0.1.2.tar.gz |
| Windows binaries: | r-devel: sae.prop_0.1.2.zip, r-release: sae.prop_0.1.2.zip, r-oldrel: sae.prop_0.1.2.zip |
| macOS binaries: | r-release (arm64): sae.prop_0.1.2.tgz, r-oldrel (arm64): sae.prop_0.1.2.tgz, r-release (x86_64): sae.prop_0.1.2.tgz, r-oldrel (x86_64): sae.prop_0.1.2.tgz |
| Old sources: | sae.prop archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.