tgp-package {tgp}R Documentation

The Treed Gaussian Process Model Package

Description

A Bayesian nonstationary nonparametric regression and design package implementing an array of models of varying flexibility and complexity.

Details

This package implements Bayesian nonstationary, semiparametric nonlinear regression with “treed Gaussian process models” with jumps to the limiting linear model (LLM). The package contains functions which facilitate inference for seven regression models of varying complexity using Markov chain Monte Carlo (MCMC): linear model, CART (Classification and Regression Tree), treed linear model, Gaussian process (GP), GP with jumps to the LLM, treed GP, and treed GP LLM. R provides an interface to the C/C++ backbone, and a serves as mechanism for graphically visualizing the results of inference and posterior predictive surfaces under the models. A Bayesian Monte Carlo based sensitivity analysis is implemented, and multi-resolution models are also supported. A limited set of experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement.

For a fuller overview including a complete list of functions, demos and vignettes, please use help(package="tgp").

Author(s)

Robert B. Gramacy, rbgramacy@ams.ucsc.edu
Matt Taddy, taddy@ams.ucsc.edu

References

Gramacy, R. B. (2007). tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. Journal of Statistical Software, 19(9). http://www.jstatsoft.org/v19/i09

Gramacy, R. B., Lee, H. K. H. (2006). Bayesian treed Gaussian process models. Available as UCSC Technical Report ams2006-01.

Gramacy, R. B., Lee, H. K. H. (2006). Adaptive design of supercomputer experiments. Available as UCSC Technical Report ams2006-02.

Gramacy, R.B., Samworth, R.J., and King, R. (2007) Importance Tempering. ArXiV article 0707.4242 http://arxiv.org/abs/0707.4242

Gray, G.A., Martinez-Canales, M., Taddy, M.A., Lee, H.K.H., and Gramacy, R.B. (2007) Enhancing Parallel Pattern Search Optimization with a Gaussian Process Oracle, SAND2006-7946C, Proceedings of the NECDC

http://www.ams.ucsc.edu/~rbgramacy/tgp.html


[Package tgp version 2.0-1 Index]