tgp-package {tgp} | R Documentation |
A Bayesian nonstationary nonparametric regression and design package implementing an array of models of varying flexibility and complexity.
This package implements Bayesian nonparametric and nonstationary regression with “treed Gaussian process models”. The package contains functions which facilitate inference for seven regression models of varying complexity using Markov chain Monte Carlo (MCMC): linear model, linear CART (Classification and Regression Tree), Gaussian process (GP), GP with jumps to the limiting linear model (LLM), treed GP, and treed GP LLM. R provides an interface to the C/C++ backbone, and also provides a mechanism for graphically visualizing the results of inference and posterior predictive surfaces under the models. A limited set of experimental design and adaptive sampling functions are also provided.
For a complete list of functions, use library(help="tgp")
.
Robert B. Gramacy rbgramacy@ams.ucsc.edu
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.
http://www.ams.ucsc.edu/~rbgramacy/tgp.html