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Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.
Version: | 0.1.1 |
Depends: | R (≥ 4.00) |
Imports: | cli, dplyr, ggplot2, methods, rlang, stats, tidyr |
Published: | 2024-10-24 |
DOI: | 10.32614/CRAN.package.saens |
Author: | Ridson Al Farizal P [aut, cre, cph], Azka Ubaidillah [aut] |
Maintainer: | Ridson Al Farizal P <alfrzlp at gmail.com> |
BugReports: | https://github.com/Alfrzlp/sae-ns/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/Alfrzlp/sae-ns |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | saens results |
Reference manual: | saens.pdf |
Package source: | saens_0.1.1.tar.gz |
Windows binaries: | r-devel: saens_0.1.1.zip, r-release: saens_0.1.1.zip, r-oldrel: saens_0.1.1.zip |
macOS binaries: | r-release (arm64): saens_0.1.1.tgz, r-oldrel (arm64): saens_0.1.1.tgz, r-release (x86_64): saens_0.1.1.tgz, r-oldrel (x86_64): saens_0.1.1.tgz |
Old sources: | saens 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.