Differential Expression Analysis Using a Bottom-Up Model
c(person("Gershom", "Buri", email = "buri@aims.edu.gh", role = c("aut","cre")),person("Wilfred", "Ndifon", email = "ndifon@gmail.com", role = "aut"))
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of  a bottom-up model for PCR amplification. This model is  developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
Gershom Buri [aut, cre],
Wilfred Ndifon [aut]

Julie Josse, Sylvain Sardy, Stefan Wager
Estimate a low rank matrix from noisy data using singular values
thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.

Estimate a low rank matrix from noisy data using singular values
thresholding and shrinking functions. Impute missing values with matrix completion.

Statistical Analyses of De Novo Genetic Variants
c(
person("James","Ware",email="j.ware@imperial.ac.uk",role=c("aut","cre")),
person("Jason","Homsy",role="ctb"),
person("Kaitlin","Samocha",role="ctb")
)
An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants.  denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size.  Observed variant frequencies can then be compared against expectation in a Poisson framework.  denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.
James Ware [aut, cre],
Jason Homsy [ctb],
Kaitlin Samocha [ctb]

An integrated toolset for the analysis of de novo (sporadic)
genetic sequence variants. denovolyzeR implements a mutational model that
estimates the probability of a de novo genetic variant arising in each human
gene, from which one can infer the expected number of de novo variants in a
given population size. Observed variant frequencies can then be compared against
expectation in a Poisson framework. denovolyzeR provides a suite of functions
to implement these analyses for the interpretation of de novo variation in human
disease.