Package: MRwarping
Type: Package
Title: Multiresolution time warping for functional data.
Version: 1.0
Date: 2013-08-16
Author: L. Slaets, G. Claeskens, B.W. Silverman
Maintainer: Gerda Claeskens <Gerda.Claeskens@kuleuven.be>
Description: The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.
Depends: boa, SemiPar
License: GPL-2
LazyLoad: yes
Packaged: 2013-10-11 06:44:26 UTC; ndbaf45
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-10-11 12:54:06
Package: BayHap
Type: Package
Title: Bayesian analysis of haplotype association using Markov Chain
Monte Carlo
Version: 1.0.1
Date: 2013-03-13
Author: Raquel Iniesta and Victor Moreno
Maintainer: Raquel Iniesta <riniesta@pssjd.org>
Description: The package BayHap performs simultaneous estimation of
uncertain haplotype frequencies and association with haplotypes
based on generalized linear models for quantitative, binary and
survival traits. Bayesian statistics and Markov Chain Monte
Carlo techniques are the theoretical framework for the methods
of estimation included in this package. Prior values for model
parameters can be included by the user. Convergence diagnostics
and statistical and graphical analysis of the sampling output
can be also carried out.
Depends: boa
License: GPL (>= 2)
Packaged: 2013-03-13 15:33:49 UTC; riniesta
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-03-13 21:25:31
Package: bayesMCClust
Type: Package
Title: Mixtures-of-Experts Markov Chain Clustering and Dirichlet
Multinomial Clustering
Version: 1.0
Date: 2012-01-26
Author: Christoph Pamminger <christoph.pamminger@gmail.com>
Maintainer: Christoph Pamminger <christoph.pamminger@gmail.com>
Description: This package provides various Markov Chain Monte Carlo
(MCMC) sampler for model-based clustering of discrete-valued
time series obtained by observing a categorical variable with
several states (in a Bayesian approach). In order to analyze
group membership, we provide also an extension to the
approaches by formulating a probabilistic model for the latent
group indicators within the Bayesian classification rule using
a multinomial logit model.
Depends: R (>= 2.14.1), gplots, xtable, grDevices, mnormt, MASS, bayesm, boa, e1071, gtools
Suggests: nnet
License: GPL-2
LazyLoad: yes
Packaged: 2012-01-31 09:48:34 UTC; AK107357
Repository: CRAN
Date/Publication: 2012-01-31 10:57:02
providing 
.