tgp (2.4-14)

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Bayesian Treed Gaussian Process Models.

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
Author(s): Robert B. Gramacy <> and Matt A. Taddy <>

License: LGPL

Uses: maptree, MASS
Reverse depends: bootfs, c060, CompModSA, earlywarnings, penalizedSVM, plgp, RNCEP
Reverse suggests: BayesTreePrior, diversitree, dynaTree, IGP, mlr, rmi, SPOT, ssr

Released about 4 years ago.

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