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.

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 in this package

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

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

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