reglogit (version 1.2-5)

reglogit-package: Simulation-based Regularized Logistic Regression

Description

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.

Details

Package:	reglogit
Type:	Package
Version:	1.0
Date:	2011-08-05
License:	LGPL
LazyLoad:	yes

See the documentation for the reglogit function

References

R.B. Gramacy, N.G. Polson. “Simulation-based regularized logistic regression”. (2010); arXiv:1005.3430; http://arxiv.org/abs/1005.3430

See Also

reglogit, blasso and regress

Examples

# NOT RUN {
## see the help file for the reglogit function
# }