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

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.

Georgios Papageorgiou
2018-12-08

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-069.zip

CRAN packages used

BNSP, bamlss, spikeSlabGAM, brms, gamboostLSS, mgcv, coda, ggplot2, plot3D, threejs, colorspace, np, gamair, lattice

CRAN Task Views implied by cited packages

Bayesian, Graphics, Econometrics, Environmetrics, Phylogenetics, SocialSciences, gR, MachineLearning, Multivariate

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Papageorgiou, "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 = {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}
}