https://github.com/longhaisk/htlr

Bayesian Logistic Regression with Hyper-LASSO priors
https://github.com/longhaisk/htlr

bayesian classification high-dimensional-data machine-learning mcmc

Last synced: 4 months ago
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Bayesian Logistic Regression with Hyper-LASSO priors

• Host: GitHub
• URL: https://github.com/longhaisk/htlr
• Owner: longhaiSK
• License: gpl-3.0
• Created: 2019-06-18T19:36:30.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2025-10-21T04:05:51.000Z (8 months ago)
• Last Synced: 2025-10-22T03:56:48.009Z (8 months ago)
• Topics: bayesian, classification, high-dimensional-data, machine-learning, mcmc
• Language: R
• Homepage: https://longhaisk.github.io/HTLR
• Size: 34.1 MB
• Stars: 10
• Watchers: 2
• Forks: 1
• Open Issues: 5
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • License: LICENSE

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README

          
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

## HTLR: Bayesian Logistic Regression with Heavy-tailed Priors

### a test

[![CRAN status](https://www.r-pkg.org/badges/version/HTLR)](https://CRAN.R-project.org/package=HTLR)

[![build](https://github.com/longhaiSK/HTLR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/longhaiSK/HTLR/actions/workflows/R-CMD-check.yaml)

[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

[![downloads](https://cranlogs.r-pkg.org/badges/grand-total/HTLR)](https://cran.r-project.org/package=HTLR)

*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. 

## Installation

[CRAN](https://CRAN.R-project.org) version (recommended):

``` r

install.packages("HTLR")

```

Development version on [GitHub](https://github.com/):

``` r

# install.packages("devtools")

devtools::install_github("longhaiSK/HTLR")

```

This package uses library [Armadillo](https://arma.sourceforge.net/) for carrying out most of matrix operations, you may get speed benefits from using an alternative BLAS library such as [ATLAS](https://math-atlas.sourceforge.net/), [OpenBLAS](https://www.openblas.net/) or Intel MKL. Check out this [post](https://brettklamer.com/diversions/statistical/faster-blas-in-r/) for the comparison and the installation guide. Windows users may consider installing [Microsoft R Open](https://mran.microsoft.com/open).

## Reference

Longhai Li and Weixin Yao (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. \emph{Journal of Statistical Computation and Simulation} 2018, 88:14, 2827-2851, [the published version](https://www.tandfonline.com/doi/full/10.1080/00949655.2018.1490418), or [arXiv version](https://arxiv.org/pdf/1405.3319.pdf).