Package: tgp
Title: Bayesian treed Gaussian process models
Version: 2.4-2
Date: 2011-03-30
Author: Robert B. Gramacy <rbgramacy@ams.ucsc.edu> and Matt A. Taddy
        <taddy@ams.ucsc.edu>
Depends: R (>= 2.10)
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@ams.ucsc.edu>
License: LGPL
URL: http://www.ams.ucsc.edu/~rbgramacy/tgp.html
Repository: CRAN
Date/Publication: 2011-04-08 13:50:15