Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1-1
Date: 2011-04-07
Author: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Maintainer: Robert B. Gramacy <rbgramacy@chicagobooth.edu>
Description: 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
Depends: R (>= 2.4), mvtnorm, tgp
Suggests: akima, ellipse, splancs
License: LGPL
URL: http://faculty.chicagobooth.edu/robert.gramacy/plgp.html
Packaged: 2011-04-08 13:25:29 UTC; bobby
Repository: CRAN
Date/Publication: 2011-04-08 13:50:11

Description: Sequential Monte Carlo (SMC) inference for fully Bayesian
particle learning (PL) following Gramacy & Polson (2011) <arXiv:0909.5262>.
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
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
provide for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
See the examples section of ?plgp and demo(package="plgp")