ape, geiger, snowfall, TreeSim, methods, ouch, reshape2

Monte Carlo based model choice for applied phylogenetics of
continuous traits. Method described in  Carl Boettiger, Graham Coop,
Peter Ralph (2012) Is your phylogeny informative? Measuring
the power of comparative methods, Evolution 66 (7)
2240-51. doi:10.1111/j.1558-5646.2011.01574.x.
person('Carl', 'Boettiger', email='cboettig@gmail.com', role =c('aut', 'cre'))
dplyr, geiger, ggplot2, parallel, ouch, tidyr

Monte Carlo based model choice for applied phylogenetics of
continuous traits. Method described in  Carl Boettiger, Graham Coop,
Peter Ralph (2012) Is your phylogeny informative? Measuring
the power of comparative methods, Evolution 66 (7)
2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.
covr, gridExtra, knitr, testthat, rmarkdown

Parsimonious Mixtures of Contaminated Gaussian Distributions

The pmclust aims to utilize model-based clustering
(unsupervised) for high dimensional and ultra large data,
especially in a distributed manner. The package employs Rmpi to
perform a parallel version of expectation and maximization (EM)
algorithm for finite mixture Gaussian models. The unstructured
dispersion matrices are assumed in the Gaussian models. The
implementation is default in the single program multiple data
(SPMD) programming model. The code can be executed through Rmpi
and independent to most MPI applications. See the High
Performance Statistical Computing (HPSC) website for more
information, documents and examples.

c(person("Wei-Chen", "Chen", role = c("aut", "cre"), email =
"wccsnow@gmail.com"), person("George", "Ostrouchov", role =
"aut"))
R (>= 2.13.0), methods, rlecuyer, pbdMPI, MASS, MixSim
The pmclust aims to utilize model-based clustering
(unsupervised) for high dimensional and ultra large data,
especially in a distributed manner. The package employs Rmpi to
perform a expectation-gathering-maximization (EGM) algorithm
for finite mixture Gaussian models. The unstructured dispersion
matrices are assumed in the Gaussian models. The implementation
is default in the single program multiple data (SPMD)
programming model. The code can be executed through Rmpi and
independent to most MPI applications. See the High Performance
Statistical Computing (HPSC) website for more information,
documents and examples.
Wei-Chen Chen [aut, cre], George Ostrouchov [aut]