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 [issues need fixing before 2024-10-23] |
Documentation:
Downloads:
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