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 |
Reference manual: | mcmcsae.pdf |
Vignettes: |
Basic area-level model Linear regression, prediction, and survey weighting Basic unit-level models |
Package source: | mcmcsae_0.7.0.tar.gz |
Windows binaries: | r-devel: 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-oldrel (arm64): mcmcsae_0.7.0.tgz, r-release (x86_64): mcmcsae_0.7.0.tgz, r-oldrel (x86_64): mcmcsae_0.7.0.tgz |
Old sources: | mcmcsae archive |
Reverse suggests: | hbsae |
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