reglogit: Simulation-Based Regularized Logistic Regression

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.

Install the latest version of this package by entering the following in R:
install.packages("reglogit")
AuthorRobert B. Gramacy <rbgramacy@chicagobooth.edu>
Date of publication2015-06-22 20:26:49
MaintainerRobert B. Gramacy <rbgramacy@chicagobooth.edu>
LicenseLGPL
Version1.2-4
http://faculty.chicagobooth.edu/robert.gramacy/reglogit.html

View on CRAN

Files

src
src/Makevars
src/randomkit.h
src/randomkit.c
src/gibbs.c
src/rand_draws.h
src/rand_draws.c
NAMESPACE
demo
demo/spam_dRUM.R
demo/00Index
demo/pima_dRUM.R
data
data/pima.rda
R
R/dRUM.R R/reglogit.R
MD5
DESCRIPTION
ChangeLog
man
man/reglogit.Rd man/reglogit-package.Rd man/pima.Rd man/reglogit-internal.Rd man/predict.reglogit.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.