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Package: tgp
Title: Bayesian treed Gaussian process models
Version: 2.4-9
Date: 2013-04-01
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu> and Matt A.
Taddy <taddy@chicagobooth.edu>
Depends: R (>= 2.14.0)
Suggests: akima, maptree, MASS
Description: Bayesian nonstationary, semiparametric nonlinear
regression and design by treed Gaussian processes (GPs) with
jumps to the limiting linear model (LLM). Special cases also
implemented include Bayesian linear models, CART, treed linear
models, stationary separable and isotropic GPs, and GP
single-index models. Provides 1-d and 2-d plotting functions
(with projection and slice capabilities) and tree drawing,
designed for visualization of tgp-class output. Sensitivity
analysis and multi-resolution models are supported. Sequential
experimental design and adaptive sampling functions are also
provided, including ALM, ALC, and expected improvement. The
latter supports derivative-free optimization of noisy black-box
functions.
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
License: LGPL
URL: http://www.ams.ucsc.edu/~rbgramacy/tgp.html
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-04-04 20:46:33
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