GBASS - An Emulator for Stochastic Computer Models

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Description

GBASS (Generalized Bayesian Adaptive Smoothing Splines) is an R package for fitting BASS-style models with flexible likelihoods, including the Student’s $t$, Horseshoe, asymmetric Laplace (for quantile regression), and Normal-Wald likelihoods. The package provides an implementation of the methods proposed in Rumsey et al. (2023), while retaining a familiar interface for users of BASS.

To work directly with gbass(), priors for the global variance factor $w$ and local variance factors $v_i$ should be specified using either a generalized inverse Gaussian (GIG) prior or a generalized beta prime (GBP) prior. Helpful wrappers tbass(), qbass(), hbass(), and nwbass() are also provided for important special cases.

Installation

To install the GBASS package, type

# install.packages("remotes")
remotes::install_github("knrumsey/GBASS")

Example

The example below compares nwbass() to a standard bass() model on a simple stochastic emulator problem with skewed response behavior.

#> 
#> Attaching package: 'BASS'
#> The following object is masked from 'package:GBASS':
#> 
#>     sobol
#> Warning: package 'lhs' was built under R version 4.4.3

In this example, nwbass() is able to capture the asymmetric predictive distribution much better than a Gaussian bass() model.

References

Rumsey, K.N., Francom, D. and Shen, A., 2024. Generalized Bayesian MARS: Tools for stochastic computer model emulation. SIAM/ASA Journal on Uncertainty Quantification, 12(2), pp.646-666.

Copyright Notice

© 2021. Triad National Security, LLC. All rights reserved.

This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S. Department of Energy/National Nuclear Security Administration. All rights in the program are reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear Security Administration. The Government is granted for itself and others acting on its behalf a nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare derivative works, distribute copies to the public, perform publicly and display publicly, and to permit others to do so.