The R Journal: accepted article

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BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection PDF download
Georgios Papageorgiou

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.

Received: 2018-07-31; online 2018-12-08, supplementary material, (3.1 Kb)
CRAN packages: BNSP, bamlss, spikeSlabGAM, brms, gamboostLSS, mgcv, coda, ggplot2, plot3D, threejs, colorspace, np, gamair, lattice
CRAN Task Views implied by cited CRAN packages: Bayesian, Graphics, Econometrics, Environmetrics, Phylogenetics, SocialSciences, gR, MachineLearning, Multivariate


CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-069,
  author = {Georgios Papageorgiou},
  title = {{BNSP: an R Package for Fitting Bayesian Semiparametric
          Regression Models and Variable Selection}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-069},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-069/index.html}
}