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saens: Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas

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: 0.1.2
Depends: R (≥ 4.00)
Imports: cli, dplyr, ggplot2, methods, rlang, stats, tidyr
Published: 2024-11-18
DOI: 10.32614/CRAN.package.saens
Author: Ridson Al Farizal P ORCID iD [aut, cre, cph], Azka Ubaidillah ORCID iD [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 NEWS
CRAN checks: saens results

Documentation:

Reference manual: saens.pdf

Downloads:

Package source: saens_0.1.2.tar.gz
Windows binaries: r-devel: saens_0.1.2.zip, r-release: saens_0.1.2.zip, r-oldrel: saens_0.1.2.zip
macOS binaries: r-release (arm64): saens_0.1.2.tgz, r-oldrel (arm64): saens_0.1.2.tgz, r-release (x86_64): saens_0.1.2.tgz, r-oldrel (x86_64): saens_0.1.2.tgz
Old sources: saens archive

Linking:

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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.