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: 0.2.1
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), MASS (≥ 7.3-60.2), rmarkdown (≥ 2.26), rstanarm (≥ 2.32.1), testthat (≥ 3.0.0)
Published: 2025年01月08日
Author: David M. Dalenberg [aut, cre]
Maintainer: David M. Dalenberg <dalenbe2 at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: bayesMeanScale results

Documentation:

Downloads:

Windows binaries: r-devel: bayesMeanScale_0.2.1.zip, r-release: bayesMeanScale_0.2.1.zip, r-oldrel: bayesMeanScale_0.2.1.zip
macOS binaries: r-release (arm64): bayesMeanScale_0.2.1.tgz, r-oldrel (arm64): bayesMeanScale_0.2.1.tgz, r-release (x86_64): bayesMeanScale_0.2.1.tgz, r-oldrel (x86_64): bayesMeanScale_0.2.1.tgz

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