bayesMeanScale: Bayesian Post-Estimation on the Mean Scale

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.

Version: 0.1.3
Depends: R (≥ 3.5.0)
Imports: bayestestR (≥ 0.13.2), data.table (≥ 1.15.2), magrittr (≥ 2.0.3), posterior (≥ 1.5.0)
Suggests: flextable (≥ 0.9.5), knitr (≥ 1.45), rmarkdown (≥ 2.26), rstanarm (≥ 2.32.1), testthat (≥ 3.0.0)
Published: 2024-05-23
Author: David M. Dalenberg [aut, cre]
Maintainer: David M. Dalenberg <dalenbe2 at gmail.com>
BugReports: https://github.com/dalenbe2/bayesMeanScale/issues
License: GPL (≥ 3)
URL: https://github.com/dalenbe2/bayesMeanScale
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bayesMeanScale results

Documentation:

Reference manual: bayesMeanScale.pdf
Vignettes: Introduction to 'bayesMeanScale'

Downloads:

Package source: bayesMeanScale_0.1.3.tar.gz
Windows binaries: r-devel: bayesMeanScale_0.1.2.zip, r-release: bayesMeanScale_0.1.1.zip, r-oldrel: bayesMeanScale_0.1.2.zip
macOS binaries: r-release (arm64): bayesMeanScale_0.1.3.tgz, r-oldrel (arm64): bayesMeanScale_0.1.3.tgz, r-release (x86_64): bayesMeanScale_0.1.3.tgz, r-oldrel (x86_64): bayesMeanScale_0.1.3.tgz
Old sources: bayesMeanScale archive

Linking:

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