Description: A Maximum Likelihood and bootstrap approach to mixed models

Description: A Maximum Likelihood and bootstrap approach to mixed

Author: Göran Broström and Henrik Holmberg

Description: Binary and Poisson regression for clustered data, fixed

Title: Generalized Linear Models with Clustering
Description: Binomial and Poisson regression for clustered data, fixed

person("Jianming", "Jin", role = "ctb"),
person("Henrik", "Holmberg", role = "ctb"))

Title: High Dimensional Penalized Generalized Linear Mixed Models
person("Hillary", "Heiling", email = "hheiling@live.unc.edu", role = c("aut", "cre")),
person("Naim", "Rashid", email = "naim@unc.edu", role = c("aut")),
person("Quefeng", "Li", email = "quefeng@email.unc.edu", role = c("aut")))
Description: Fits high dimensional penalized generalized linear
the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm.
The purpose of the package is to perform variable selection on both the fixed and
random effects simultaneously for generalized linear mixed models.
The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
The techniques used in the minimization portion of the procedure (the M-step) are
derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011)
<doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
appropriate modifications to account for the estimation and penalization of
the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD,
Imports: ggplot2, Matrix, methods, ncvreg, reshape2, rstan (>= 2.18.1),
rstantools (>= 2.0.0), stringr, mvtnorm, MASS
Depends: lme4, bigmemory, Rcpp (>= 0.12.0), R (>= 3.6.0)
LinkingTo: BH (>= 1.66.0), bigmemory, Rcpp (>= 0.12.0), RcppArmadillo,

Title: General Linear Mixed Models for Gene-Level Differential
person(given = "Elisabetta",family = "Sciacca",
person(given = "Anna", family = "Surace",
Description: Using random and fixed effects to model expression at an individual gene level can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, therefore do not capture interaction terms. This package uses negative binomial mixed effects models to fit gene expression with matched samples. This is particularly useful for investigating  changes in gene expression between groups of individuals over time, as seen in: Rivellese F., Surace A.E.A.,  Goldmann K., Sciacca E., Giorli G., Cubuk C., John C.R.,  Nerviani A., Fossati-Jimack L., Thorborn G., Humby F., Bombardieri M.,  Lewis M.J.,  Pitzalis C. (2021) "Molecular Pathology Profiling of Synovial Tissue Predicts Response to Biologic Treatment in Rheumatoid Arthritis" [Manuscript in preparation].
Imports: MASS, car, stats, gghalves, ggplot2, ggpubr, graphics, lme4,
Author: Myles Lewis [aut] (<https://orcid.org/0000-0001-9365-5345>),
Katriona Goldmann [aut, cre] (<https://orcid.org/0000-0002-9073-6323>),