Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Devin Francom, Bruno Sansó
Title: BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces
Abstract: We present the R package BASS as a tool for nonparametric regression. The primary focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models and performing global sensitivity analyses of these models. The BASS framework is similar to that of Bayesian multivariate adaptive regression splines (BMARS) from Denison, Mallick, and Smith (1998), but with many added features. The software is built to efficiently handle significant amounts of data with many continuous or categorical predictors and with functional response. Under our Bayesian framework, most priors are automatic but these can be modified by the user to focus on parsimony and the avoidance of overfitting. If directed to do so, the software uses parallel tempering to improve the reversible jump Markov chain Monte Carlo (RJMCMC) methods used to perform inference. We discuss the implementation of these features and present the performance of BASS in a number of analyses of simulated and real data.

Page views:: 444. Submitted: 2017-01-10. Published: 2020-09-08.
Paper: BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces     Download PDF (Downloads: 90)
Supplements:
BASS_1.2.2.tar.gz: R source package Download (Downloads: 6; 3MB)
v94i08.R: R replication code Download (Downloads: 5; 12KB)
airfoil_self_noise.dat: Replication data Download (Downloads: 5; 58KB)

DOI: 10.18637/jss.v094.i08

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.