Bayesian Logistic Regression with Hyper-LASSO priors
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):
install.packages("HTLR")
Development version on GitHub:
# install.packages("devtools")devtools::install_github("longhaiSK/HTLR")
This package uses library Armadillo for
carrying out most of matrix operations, you may get speed benefits from
using an alternative BLAS library such as
ATLAS,
OpenBLAS or Intel MKL. Check out this
post
for the comparison and the installation guide. Windows users may
consider installing Microsoft R Open.
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.