DataCombine,ggplot2,lmerTest, sjstats (>= 0.10.2), anytime,
plyr

Evolutionary Monte Carlo (EMC) algorithm
Random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement

Random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement.

random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement

Evolutionary Monte Carlo (EMC) methods for clustering
evolutionary Monte Carlo methods for clustering, temperature ladder construction and placement

evolutionary Monte Carlo methods for clustering,
temperature ladder construction and placement. For mclust see
http://cran.r-project.org/web/packages/mclust/LICENSE

Evolutionary Monte Carlo (EMC) Methods for Clustering
Evolutionary Monte Carlo methods for clustering, temperature
ladder construction and placement. This package implements methods
introduced in Goswami, Liu and Wong (2007) <doi:10.1198/106186007X255072>.
The paper above introduced probabilistic genetic-algorithm-style crossover
moves for clustering. The paper applied the algorithm to several clustering
problems including Bernoulli clustering, biological sequence motif
clustering, BIC based variable selection, mixture of Normals clustering,
and showed that the proposed algorithm performed better both as a sampler
and as a stochastic optimizer than the existing tools, namely, Gibbs sampling,
``split-merge'' Metropolis-Hastings algorithm, K-means clustering, and the
MCLUST algorithm (in the package 'mclust').

EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("Ranjan", "Maitra", role = "aut"),
person("Volodymyr", "Melnykov", role = "aut"))
EMCluster provides EM algorithms and several efficient
initialization methods for model-based clustering of finite
mixture Gaussian distribution with unstructured dispersion in
both of unsupervised and semi-supervised learnings.
Wei-Chen Chen [aut, cre], Ranjan Maitra [aut], Volodymyr
Melnykov [aut]