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
CRAN checks: bayesloglin results

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Reference manual: bayesloglin.pdf
Vignettes: bayesloglin-R-package
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
OS X El Capitan binaries: r-release: bayesloglin_1.0.1.tgz
OS X Mavericks binaries: r-oldrel: bayesloglin_1.0.1.tgz
Old sources: bayesloglin archive

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