HTLR: Bayesian Logistic Regression with Heavy-tailed Priors

CRAN status Lifecycle: maturing Build Status

HTLR performs classification and feature selection by fitting Bayesian 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 with little punishment. Heavy-tailed priors 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.


CRAN version (recommended):


Development version on GitHub:

# install.packages("devtools")


Longhai Li and Weixin Yao (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. 2018, 88:14, 2827-2851, the published version, or arXiv version.