person("Jonathan", "Sidi", email = "yoni.sidi@mail.huji.ac.il", role = c("aut", "cre"))
Fits (Gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using the linear mixed model elastic-net penalty.
quadprog, mvtnorm, glmnet, lme4, utils, stats, glmmLasso

Goodness-of-fit-measures for linear mixed models with
one-level-grouping
Package lmmfit contains three functions evaluating some
goodness-of-fit-measures for linear mixed models with
one-level-grouping fitted using lme() from package nlme.

This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
for the fixed-effects parameter.

Fit right censored Multiple Ordinal Tobit (MOT) model.

c(
person("Fulya", "Gokalp Yavuz", email = "fulyagokalp@gmail.com", role = c("aut", "cre")),
person("Barret", "Schloerke", email = "schloerke@gmail.com", role = c("aut"))
)
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
MASS, matrixcalc, mnormt, plyr, doParallel, bigmemory
Fulya Gokalp Yavuz [aut, cre],
Barret Schloerke [aut]

Linear mixed effect model splines for modelling and analysis of
time course data
stats, methods, nlme, lmeSplines, parallel, reshape, gdata,
graphics
Jasmin Straube, Kim-Anh Le Cao and Emma Huang with contributions of
Dominique Gorse
The package implements linear mixed effect model splines for
modelling and differential expression for highly dimensional data sets:
lmmSpline to models time course expression profiles and lmmsDE performs
differential expression analysis to identify differential expression
between groups, time and/or time x group interaction.

stats, methods, nlme, lmeSplines, parallel, reshape, gdata,
gplots, ggplot2, graphics
The package implements linear mixed effect model splines for
modelling and differential expression for highly dimensional data sets:
investNoise for quality control and filterNoise for removing
non-informative trajectories; lmmSpline to model time course expression
profiles and lmmsDE performs differential expression analysis to identify
differential expression between groups, time and/or time x group
interaction.