calc.ecis

calc.eis

calc.ents

calc.ieci

calc.iecis

calc.alcs

calc.vars

calc.ktKik.x

calc2.ktKik.x

covar

covar.sep

covar.sim

cv.folds

dist2covar.symm

distance

ei.adapt

entropy

entropy.adapt

entropy.bvsb

findmin.ConstGP

findmin.GP

hist.particle.params

ieci.adapt

alc.adapt

mindist.adapt

var.adapt

ieci.const.adapt

alc.const.adapt

ieci.ConstGP

alc.ConstGP

ieci.GP

alc.GP

lpost.GP

PL.clear

pred.mean.GP

resample

tquants

unif.propose.pos

mvnorm.propose.rw

updat.GP

util.GP

renorm.weights

renorm.lweights

latents.CGP

pall

peach

psave

Xcand

phist

internal

plgp-internal

Sequential Monte Carlo inference for fully Bayesian
        Gaussian process (GP) regression and classification models by
        particle learning (PL).  The sequential nature of inference and
        the active learning (AL) hooks provided facilitate thrifty
        sequential design (by entropy) and optimization (by
        improvement) for classification and regression models,
        respectively. This package essentially provides a generic PL
        interface, and functions (arguments to the interface) which
        implement the GP models and AL heuristics.  Functions for a
        special, linked, regression/classification GP model and an
        integrated expected conditional improvement (IECI) statistic is
        provides for optimization in the presence of unknown
        constraints. Separable and isotropic Gaussian, and single-index
        correlation functions are supported. See the examples section
        of ?plgp and demo(package="plgp") for an index of demos

plgp-internal: Internal plgp Functions

Description

Arguments

Details