Bayesian Logistic Regression with Heavy-Tailed Priors




CRAN status Lifecycle: maturing Build Status License: GPL v2

HTLR performs classification and feature selection by fitting Baeysian polychotomous (multiclass, multinomial) logistic regression models based on heavy-tailed priors with small degree freedom. This package is suitable for classification with high-dimensional features, such as gene expression profiles. Heavy-tailed priors can impose stronger shrinkage (compared to Guassian and Laplace priors) to the coefficients associated with a large number of useless features, but still allow coefficients of a small number of useful features to stand out without punishment. It can also automatically make selection within a large number of correlated features. The posterior of coefficients and hyperparameters is sampled with resitricted Gibbs sampling for leveraging high-dimensionality and Hamiltonian Monte Carlo for handling high-correlations among coefficients.

This site focuses mainly on illustrating the usage and syntax of HTLR. For more details on the algorithm, see the original article: <DOI:10.1080/00949655.2018.1490418> (PDF).


You can install the released version of HTLR from CRAN with:


And the development version from GitHub with:

# install.packages("devtools")