SAEforest: Mixed Effect Random Forests for Small Area Estimation

Mixed Effects Random Forests (MERFs) are a data-driven, nonparametric alternative to current methods of Small Area Estimation (SAE). 'SAEforest' provides functions for the estimation of regionally disaggregated linear and nonlinear indicators using survey sample data. Included procedures facilitate the estimation of domain-level economic and inequality metrics and assess associated uncertainty. Emphasis lies on straightforward interpretation and visualization of results. From a methodological perspective, the package builds on approaches discussed in Krennmair and Schmid (2022) <arXiv:2201.10933v2> and Krennmair et al. (2022) <arXiv:2204.10736>.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: caret, dplyr, ggplot2, haven, ineq, lme4, maptools, pbapply, pdp, ranger, reshape2, stats, vip
Suggests: R.rsp, sp, rgeos, testthat (≥ 3.0.0)
Published: 2022-09-07
Author: Patrick Krennmair [aut, cre]
Maintainer: Patrick Krennmair <patrick.krennmair at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: see file COPYRIGHTS
NeedsCompilation: no
Materials: README
CRAN checks: SAEforest results


Reference manual: SAEforest.pdf
Vignettes: The R Package SAEforest


Package source: SAEforest_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): SAEforest_1.0.0.tgz, r-oldrel (arm64): SAEforest_1.0.0.tgz, r-release (x86_64): SAEforest_1.0.0.tgz, r-oldrel (x86_64): SAEforest_1.0.0.tgz


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