bayesloglin: Bayesian Analysis of Contingency Table Data

The function MC3() searches for log-linear models with the highest posterior probability. The function gibbsSampler() is a blocked Gibbs sampler for sampling from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.

Version:	1.0.1
Depends:	igraph
Published:	2016-12-27
Author:	Matthew Friedlander
Maintainer:	Matthew Friedlander <friedla at yorku.ca>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
In views:	Bayesian
CRAN checks:	bayesloglin results

Documentation:

Reference manual:	bayesloglin.pdf
Vignettes:	bayesloglin-R-package

Downloads:

Package source:	bayesloglin_1.0.1.tar.gz
Windows binaries:	r-devel: bayesloglin_1.0.1.zip, r-release: bayesloglin_1.0.1.zip, r-oldrel: bayesloglin_1.0.1.zip
macOS binaries:	r-release (arm64): bayesloglin_1.0.1.tgz, r-oldrel (arm64): bayesloglin_1.0.1.tgz, r-release (x86_64): bayesloglin_1.0.1.tgz, r-oldrel (x86_64): bayesloglin_1.0.1.tgz
Old sources:	bayesloglin archive

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