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rsae: Robust Small Area Estimation

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

Version: 0.3
Depends: R (≥ 3.5.0)
Imports: stats, graphics
Suggests: knitr, rmarkdown, robustbase
Published: 2024-02-06
DOI: 10.32614/CRAN.package.rsae
Author: Tobias Schoch ORCID iD [aut, cre], Burkardt John [cph] (Fortran 90 subroutine zero_rc)
Maintainer: Tobias Schoch <tobias.schoch at fhnw.ch>
BugReports: https://github.com/tobiasschoch/rsae/issues
License: GPL-3
URL: https://github.com/tobiasschoch/rsae
NeedsCompilation: yes
Citation: rsae citation info
Materials: NEWS
In views: OfficialStatistics
CRAN checks: rsae results

Documentation:

Reference manual: rsae.pdf
Vignettes: Robust Estimation and Prediction Under the Unit-Level SAE Model

Downloads:

Package source: rsae_0.3.tar.gz
Windows binaries: r-devel: rsae_0.3.zip, r-release: rsae_0.3.zip, r-oldrel: rsae_0.3.zip
macOS binaries: r-release (arm64): rsae_0.3.tgz, r-oldrel (arm64): rsae_0.3.tgz, r-release (x86_64): rsae_0.3.tgz, r-oldrel (x86_64): rsae_0.3.tgz
Old sources: rsae archive

Reverse dependencies:

Reverse suggests: maSAE, spaMM

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rsae to link to this page.

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.