gWidgets, limma, RGtk2, gWidgetsRGtk2, tcltk, cairoDevice,
grDevices, graphics, stats

limma, tcltk, RGtk2, gWidgets2, gWidgets2RGtk2, cairoDevice,
grDevices, graphics, stats

gWidgets2, tcltk, gWidgets2tcltk, tkrplot, limma,
RColorBrewer, grDevices, graphics, stats

Probabilistic latent feature analysis of two-way two-mode
frequency data
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

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

Probabilistic Feature Analysis of Two-Way Two-Mode Frequencies

c(
person("Michel", "Meulders", email = "michel.meulders@kuleuven.be", role = c("aut","cre")),
person("Philippe", "De Bruecker", email = "philippe.debruecker@kuleuven.be", role = "ctb"))
Michel Meulders [aut, cre], Philippe De Bruecker [ctb]
Functions for estimating probabilistic latent feature models with a disjunctive or a conjunctive mapping rule on (aggregated) binary three-way data.

Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.

Michel Meulders [aut, cre],
Philippe De Bruecker [ctb]

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