mcmcsae: Markov Chain Monte Carlo Small Area Estimation

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

Version: 0.7.0
Depends: R (≥ 3.2.0)
Imports: Matrix (≥ 1.2.0), Rcpp (≥ 0.11.0), methods, GIGrvg, loo (≥ 2.0.0), matrixStats
LinkingTo: Rcpp, RcppEigen, Matrix, GIGrvg
Suggests: BayesLogit, lintools, splines, spdep, maptools, bayesplot, coda, posterior, parallel, testthat, roxygen2, knitr, rmarkdown, survey
Published: 2021-10-10
Author: Harm Jan Boonstra [aut, cre], Grzegorz Baltissen [ctb]
Maintainer: Harm Jan Boonstra <hjboonstra at gmail.com>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: mcmcsae results

Documentation:

Reference manual: mcmcsae.pdf
Vignettes: Basic area-level model
Linear regression, prediction, and survey weighting
Basic unit-level models

Downloads:

Package source: mcmcsae_0.7.0.tar.gz
Windows binaries: r-devel: mcmcsae_0.7.0.zip, r-devel-UCRT: mcmcsae_0.7.0.zip, r-release: mcmcsae_0.7.0.zip, r-oldrel: mcmcsae_0.7.0.zip
macOS binaries: r-release (arm64): mcmcsae_0.7.0.tgz, r-release (x86_64): mcmcsae_0.7.0.tgz, r-oldrel: mcmcsae_0.7.0.tgz
Old sources: mcmcsae archive

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