Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1-2
Date: 2011-08-23
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: 2011-08-23 18:57:30 UTC; rgramacy
Repository: CRAN
Date/Publication: 2011-08-23 20:03:11
