R package for design of experiments for design of
genome-wide association studies.  Version 2 incorporating
quantitative traits and case-control studies. The Bayes factor
should be chosen large enough to give respectable posterior
odds. This requires Bayes factors of the order of 10^6 in
genome-wide association studies where prior odds are low.
Sample sizes needed to get this strength of evidence are
substantially higher than those from traditional power
calculations. The corresponding threshold for p-values is
substantially lower than commonly used.  For quantitative
traits ldDesign uses an existing deterministic power
calculation for detection of linkage disequilibrium between a
bi-allelic QTL and a bi-allelic marker, together with the
Spiegelhalter and Smith Bayes factor to generate designs with
power to detect effects with a given Bayes factor. For case-
control studies an asymptotic approximate Bayes factor is used
to derive an analytical power calculation in dominant,
recessive, additive and general genetic models.

Collapsed Gibbs sampling methods for topic models.
This package implements latent Dirichlet allocation (LDA)
and related models.  This includes (but is not limited to)
sLDA, corrLDA, and the mixed-membership stochastic blockmodel.
Inference for all of these models is implemented via a fast
collapsed Gibbs sampler writtten in C.  Utility functions for
reading/writing data typically used in topic models, as well as
tools for examining posterior distributions are also included.

Jonathan Chang <jonchang@fb.com>, Andrew Dai
<a.dai@ed.ac.uk>

Matrix, reshape2, ggplot2 (>= 0.9.1), penalized

Jonathan Chang <jcone@princeton.edu>, Andrew Dai
<a.dai@ed.ac.uk>

Collapsed Gibbs Sampling Methods for Topic Models
Implements latent Dirichlet allocation (LDA)
and related models.  This includes (but is not limited
to) sLDA, corrLDA, and the mixed-membership stochastic
blockmodel.  Inference for all of these models is
implemented via a fast collapsed Gibbs sampler written
in C.  Utility functions for reading/writing data
typically used in topic models, as well as tools for
examining posterior distributions are also included.
Matrix, reshape2, ggplot2 (>= 1.0.0), penalized, nnet