Adaptive Shrinkage with a Nonparametric Bayesian Lasso • bnplasso

Overview

This repository contains the R package bnplasso (developer’s version), which implements the Nonparametric Bayesian Lasso (Marin et al., 2025+).

The main routine of the package, bnplasso.lm(), returns an object of S3 class, "lmBayes", which is supported by various methods like print(), summary(), plot(), fitted(), residuals(), coef(), and predict(), allowing users to quickly visualize, evaluate, and analyze the output in a familiar fashion. The package also implements the Bayesian Lasso (Park and Casella, 2008) and the Bayesian adaptive Lasso (Leng et al., 2014), through the functions blasso.lm() and balasso.lm(), respectively.

Installation

You can install the developer’s version via devtools as:

# install.packages("devtools")
devtools::install_github("marinsantiago/bnplasso")

On the other hand, if you wish to install the package from the bnplasso folder in the supplementary materials to Marin et al. (2025+):

In R, set your working directory to the folder bnplasso.
Run the following R code:

# install.packages("devtools")
devtools::build()
devtools::install()

Usage

Guidelines and help pages for using the package functions are available here. Additional examples are available at https://github.com/marinsantiago/bnplasso-examples.

Citation

If you use any part of this package in your work, please consider citing our JCGS paper:

@article{marin_bnplasso,
  title   = {Adaptive Shrinkage with a Nonparametric Bayesian Lasso},
  author  = {Santiago Marin and Bronwyn Loong and Anton H. Westveld},
  journal = {Journal of Computational and Graphical Statistics},
  year    = {2025},
  doi     = {10.1080/10618600.2025.2572327},
}

Disclaimer

The software is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.

References

Leng, C., Tran, M.-N., and Nott, D. (2014). “Bayesian Adaptive Lasso.” Annals of the Institute of Statistical Mathematics, 66, 221–244

Marin, S., Loong, B., and Westveld, A. H. (2025+), “Adaptive Shrinkage with a Nonparametric Bayesian Lasso.” Journal of Computational and Graphical Statistics. doi:10.1080/10618600.2025.2572327

Park, T. and Casella, G. (2008). “The Bayesian Lasso.” Journal of the American Statistical Association, 103 (482), 681–686.