Description: The package can be used to estimate probabilistic latent feature models
with a disjunctive or a conjunctive mapping rule for two-way two-mode frequency data

Title: Probabilistic Feature Analysis of Two-Way Two-Mode Frequencies
Packaged: 2014-12-28 12:16:29 UTC; michel.meulders

Title: Probabilistic Latent Feature Analysis
person("Michel", "Meulders", email = "michel.meulders@kuleuven.be", role = c("aut","cre")),
person("Philippe", "De Bruecker", email = "philippe.debruecker@kuleuven.be", role = "ctb"))
Author: Michel Meulders [aut, cre], Philippe De Bruecker [ctb]
Description: Functions for estimating probabilistic latent feature models with a disjunctive or a conjunctive mapping rule on (aggregated) binary three-way data.
Packaged: 2015-12-16 13:11:35 UTC; michel.meulders

Description: Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Packaged: 2017-06-09 09:35:50 UTC; michel.meulders

Packaged: 2017-06-08 19:06:39 UTC; michel.meulders

Package: plgp
Type: Package
Title: Particle Learning of Gaussian Processes
Version: 1.0
Date: 2010-04-30
Author: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
Maintainer: Robert B. Gramacy <bobby@statslab.cam.ac.uk>
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. See the examples section of ?plgp and
demo(package="plgp") for an index of examples
Depends: R (>= 2.4), mvtnorm, tgp
Suggests: akima, ellipse, splancs
License: LGPL
URL: http://www.statslab.cam.ac.uk/~bobby/plgp.html
Packaged: 2010-04-30 16:26:12 UTC; bobby
Repository: CRAN
Date/Publication: 2010-05-01 15:59:59