Expression RNA-Seq Data Analysis Based on Linear Mixed Model
Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.
R (>= 2.15.0), edgeR, limma, MASS, parallel, stats, methods

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions.
R (>= 3.2.0), parallel, label.switching, methods

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2017) <arXiv:1708.07804> .

stats, graphics, lattice, grDevices, Rcpp, tcltk, rootSolve,
qrng, mvtnorm, gtools, parallel, label.switching, methods,
coda, future.apply, future, loo (>= 2.0.0), RColorBrewer,
bridgesampling

stats, graphics, lattice, grDevices, Rcpp, tcltk, rootSolve,
qrng, mvtnorm, gtools, parallel, label.switching, methods,
coda, future.apply, loo (>= 2.0.0), RColorBrewer,
bridgesampling

stats, graphics, lattice, grDevices, Rcpp, tcltk, rootSolve,
qrng, mvtnorm, gtools, parallel, label.switching, methods,
coda, future.apply, loo (>= 2.1.0), RColorBrewer,
bridgesampling, scales

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2017) <arXiv:1708.07804>.
stats, stats4, graphics, lattice, grDevices, Rcpp, tcltk,
qrng, mvtnorm, gtools, parallel, label.switching, methods,
coda, future.apply, loo (>= 2.1.0), RColorBrewer,
bridgesampling, scales, numDeriv

stats, stats4, graphics, lattice, grDevices, Rcpp, tcltk,
qrng, mvtnorm, gtools, parallel, label.switching, methods,
coda, future.apply, loo (>= 2.4.1), RColorBrewer,
bridgesampling, scales, numDeriv

Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.

Bayesian Association Model for Genomic Data with Missing
Covariates
This package fits a linear mixed model where the
covariates for the random effects have missing values.