Authors@R: person("Jonathan", "Sidi", email = "yoni.sidi@mail.huji.ac.il", role = c("aut", "cre"))
Description: Fits (Gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using the linear mixed model elastic-net penalty.
Imports: quadprog, mvtnorm, glmnet, lme4, utils, stats, glmmLasso
Packaged: 2017-08-14 14:34:17 UTC; jonathans

Title: Goodness-of-fit-measures for linear mixed models with
Description: 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.

Title: Linear mixed-effects models with Lasso
Description: This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
Depends: methods, emulator, miscTools, penalized

Title: Multiple Ordinal Tobit (MOT) model
Description: Fit right censored Multiple Ordinal Tobit (MOT) model.

Title: Multiple Ordinal Tobit (MOT) Model

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

Title: Linear mixed effect model splines for modelling and analysis of
Imports: stats, methods, nlme, lmeSplines, parallel, reshape, gdata,
Author: Jasmin Straube, Kim-Anh Le Cao and Emma Huang with contributions of
Description: 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.

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