Bayesian Inference for Multivariate Stochastic Differential
Equations
c(person("Martin", "Lysy", role = c("aut", "cre"),
email = "mlysy@uwaterloo.ca"),
person("JunYong", "Tong", role = "aut"),
person("Nigel", "Delaney", role = "ctb"))
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Martin Lysy [aut, cre],
JunYong Tong [aut],
Nigel Delaney [ctb]

c(person("Martin", "Lysy", role = c("aut", "cre"),
email = "mlysy@uwaterloo.ca"),
person("Feiyu", "Zhu", role = "aut"),
person("JunYong", "Tong", role = "aut"),
person("Nigel", "Delaney", role = "ctb"))
knitr, rmarkdown, testthat, RcppArmadillo, RcppProgress
Martin Lysy [aut, cre],
Feiyu Zhu [aut],
JunYong Tong [aut],
Nigel Delaney [ctb]

c(person("Martin", "Lysy", role = c("aut", "cre"),
email = "mlysy@uwaterloo.ca"),
person("Feiyu", "Zhu", role = "aut"),
person("JunYong", "Tong", role = "aut"),
person("Trevor", "Kitt", role = "ctb"),
person("Nigel", "Delaney", role = "ctb"))
Rcpp (>= 0.12.7), methods, stats, tools, whisker
Martin Lysy [aut, cre],
Feiyu Zhu [aut],
JunYong Tong [aut],
Trevor Kitt [ctb],
Nigel Delaney [ctb]

Metabolite set enrichment analysis for factor loading in
principal component analysis
This package provides functions for metabolite set
enrichment analysis (MSEA) and principal component analysis
(PCA), and converting metabolite set list from your own csv
files or KEGG's tar.gz files to XML documents.  This package is
suitable for computation of MSEA for factor loading in PCA.

Modeling non-uniformity in short-read rates in RNA-Seq data
This package implements all the methods in the paper
"Modeling non-uniformity in short-read rates in RNA-Seq data".
Especially, it implements both the iterative glm procedure for
the Poisson linear model and the training procedure of the MART
model. The cross-validation for both of the methods is also
implemented.