tgp (2.2-2)
Bayesian treed Gaussian process models.
http://www.ams.ucsc.edu/~rbgramacy/tgp.html
http://cran.r-project.org/web/packages/tgp
Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic Gaussian processes. 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 <rbgramacy@ams.ucsc.edu> and Matt A. Taddy <taddy@ams.ucsc.edu>
License: LGPL
Uses: akima, maptree
Reverse depends: bootfs, c060, CompModSA, earlywarnings, penalizedSVM, plgp, RNCEP
Reverse suggests: diversitree, dynaTree, SPOT
Released almost 4 years ago.
