Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1-4
Date: 2012-01-20
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: 2012-01-20 20:02:50 UTC; bobby
Repository: CRAN
Date/Publication: 2012-01-21 07:56:34

Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.1-5
Date: 2012-07-24
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: 2012-07-24 14:37:34 UTC; bobby
Repository: CRAN
Date/Publication: 2012-07-24 15:12:20