tree2c

tgp.postprocess

print.tgptraces

tgp.read.XX.traces

tgp.read.traces

tgp.cleanup

tgp.choose.as

tgp.choose.center

tgp.check.params

tgp.partition

tgp.get.trees

tgp.plot.tree

tgp.plot.parts.1d

tgp.plot.parts.2d

tgp.plot.slice

tgp.plot.proj

tgp.cands

framify.X

slice.interp

slice.image

slice.image.contour

slice.contour

slice.persp

mean0.range1

undo.mean0.range1

check.matrix

mr.checkrez

check.itemps

check.slice

check.proj

check.sens

getlocs

print.tgp

sens.plot

mr.plot

mr.checkres

layout.torture

<p>Internal Treed Gaussian Process Model functions</p>

 internal

tgp-internal

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>.

tgp-internal: Internal Treed Gaussian Process Model Functions

Description

Arguments

Details

References