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 fu-berlin.de> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Copyright: |
see file COPYRIGHTS |
| URL: |
https://github.com/krennpa/SAEforest,
https://krennpa.github.io/SAEforest/ |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
SAEforest results |
Documentation:
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