BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection

TL;DR

The BNSP R package offers a comprehensive tool for Bayesian semiparametric regression and variable selection, utilizing basis functions and spike-slab priors for flexible modeling and regularization.

Contribution

It introduces a unified R package that combines semiparametric regression modeling with variable selection using basis functions and spike-slab priors.

Findings

Provides functions for convergence assessment and model summarization

Enables visualization of covariate effects and prediction for new data

Supports flexible modeling of mean and variance functions

Abstract

The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to represent semiparametric covariate effects in the mean and variance functions, and spike-slab priors to perform selection and regularization of the estimated effects. In addition to the main function that performs posterior sampling, the package includes functions for assessing convergence of the sampler, summarizing model fits, visualizing covariate effects and obtaining predictions for new responses or their means given feature/covariate vectors.