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: | 0.5.0 |
| 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, parallel, testthat, roxygen2, knitr, rmarkdown, survey |
| Published: | 2020-09-01 |
| Author: | Harm Jan Boonstra [aut, cre], Grzegorz Baltissen [ctb] |
| Maintainer: | Harm Jan Boonstra <hjboonstra at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | mcmcsae results |
| Reference manual: | mcmcsae.pdf |
| Vignettes: |
Area-level models Linear regression and linear weighting Unit-level models |
| Package source: | mcmcsae_0.5.0.tar.gz |
| Windows binaries: | r-devel: mcmcsae_0.5.0.zip, r-release: mcmcsae_0.5.0.zip, r-oldrel: mcmcsae_0.5.0.zip |
| macOS binaries: | r-release: mcmcsae_0.5.0.tgz, r-oldrel: mcmcsae_0.5.0.tgz |
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