BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces
Dublin Core
Title
BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces
Subject
splines, functional data analysis, sensitivity analysis, nonparametric regression.
Description
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.
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.
Creator
Devin Francom
Source
https://www.jstatsoft.org/article/view/v094i08
Publisher
Los Alamos National Laboratory
Date
June 2020
Contributor
Fajat bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Devin Francom, “BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces,” Repository Horizon University Indonesia, accessed May 14, 2025, https://repository.horizon.ac.id/items/show/8141.