Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface.
| Version: | 1.2-6 |
| Depends: | R (≥ 2.14.0), methods, mvtnorm, boot, Matrix |
| Suggests: | plgp |
| Published: | 2018-09-14 |
| Author: | Robert B. Gramacy |
| Maintainer: | Robert B. Gramacy <rbg at vt.edu> |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| URL: | http://bobby.gramacy.com/r_packages/reglogit |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | reglogit results |
| Reference manual: | reglogit.pdf |
| Package source: | reglogit_1.2-6.tar.gz |
| Windows binaries: | r-devel: reglogit_1.2-6.zip, r-release: reglogit_1.2-6.zip, r-oldrel: reglogit_1.2-6.zip |
| macOS binaries: | r-release: reglogit_1.2-6.tgz, r-oldrel: reglogit_1.2-6.tgz |
| Old sources: | reglogit archive |
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