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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 |
| 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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