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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-069.zip
BNSP, bamlss, spikeSlabGAM, brms, gamboostLSS, mgcv, coda, ggplot2, plot3D, threejs, colorspace, np, gamair, lattice
Bayesian, Graphics, Econometrics, Environmetrics, Phylogenetics, SocialSciences, gR, MachineLearning, Multivariate
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For attribution, please cite this work as
Papageorgiou, "The R Journal: BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection", The R Journal, 2018
BibTeX citation
@article{RJ-2018-069,
author = {Papageorgiou, Georgios},
title = {The R Journal: BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-069},
doi = {10.32614/RJ-2018-069},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {526-548}
}