Package: ldDesign
Version: 2.0-1
Title: Design of experiments for detection of linkage disequilibrium
Author: Rod Ball <rod.ball@scionresearch.com>
Maintainer: ORPHANED
Description: 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.
Depends:
Suggests: nlme (>= 3.1.0)
License: GPL (>= 2)
URL: mailto:rod.ball@scionresearch.com www.scionresearch.com/
Packaged: 2012-03-26 04:01:31 UTC; rod
Repository: CRAN
Date/Publication: 2012-03-26 08:06:33
NeedsCompilation: no
X-CRAN-Original-Maintainer: Rod Ball <rod.ball@scionresearch.com>
X-CRAN-Comment: Orphaned on 2013-12-18 as maintainer address
<rod.ball@scionresearch.com> bounced.

Title: Collapsed Gibbs sampling methods for topic models.
Description: 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.