elrm: Exact Logistic Regression via MCMC

Implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.

Version: 1.2.5
Depends: R (≥ 2.7.2), coda, graphics, stats
Published: 2021-10-26
DOI: 10.32614/CRAN.package.elrm
Author: David Zamar [aut, cre], Jinko Graham [aut], Brad McNeney [aut]
Maintainer: David Zamar <zamar.david at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: elrm citation info
Materials: ChangeLog
CRAN checks: elrm results


Reference manual: elrm.pdf
Vignettes: elrm


Package source: elrm_1.2.5.tar.gz
Windows binaries: r-devel: elrm_1.2.5.zip, r-release: elrm_1.2.5.zip, r-oldrel: elrm_1.2.5.zip
macOS binaries: r-release (arm64): elrm_1.2.5.tgz, r-oldrel (arm64): elrm_1.2.5.tgz, r-release (x86_64): elrm_1.2.5.tgz, r-oldrel (x86_64): elrm_1.2.5.tgz
Old sources: elrm archive


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