Package: tgp 2.4-23

tgp: 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. For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.

Authors:Robert B. Gramacy [aut, cre], Matt A. Taddy [aut]

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tgp.pdf |tgp.html✨
tgp/json (API)

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26 exports 8 stars 3.08 score 3 dependencies 12 dependents 3 mentions 203 scripts 2.8k downloads

Last updated 14 days agofrom:ee420b9727. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:bcartbgpbgpllmblmbtgpbtgpllmbtlmdefault.itempsdopt.gpexp2d.randexp2d.Zfried.boolfriedman.1.datahist2barinterp.loessitemps.barplotlhsmapTmean0.range1optim.ptgpfoptim.step.tgppartitionsenstgp.default.paramstgp.designtgp.trees

Dependencies:clustermaptreerpart