Mixed-Effects REML Incorporating Generalized Inverses
person("Matthew", "Wolak", email = "matthewwolak@gmail.com", role = c("cre", "aut"), comment = c(ORCID="0000-0002-7962-0071"))
Fit linear mixed-effects models using restricted (or residual)
maximum likelihood (REML) and with generalized inverse matrices to specify
covariance structures for random effects. In particular, the package is suited
to fit quantitative genetic mixed models, often referred to as 'animal models'
(Kruuk. 2004 <DOI: 10.1098/rstb.2003.1437>). Implements the average
information algorithm as the main tool to maximize the restricted likelihood,
but with other algorithms available (Meyer. 1997. Genet Sel Evol 29:97; Meyer
and Smith. 1998. Genet Sel Evol 28:23.).
Matthew Wolak [cre, aut] (<https://orcid.org/0000-0002-7962-0071>)

Fit linear mixed-effects models using restricted (or residual)
maximum likelihood (REML) and with generalized inverse matrices to specify
covariance structures for random effects. In particular, the package is
suited to fit quantitative genetic mixed models, often referred to as
'animal models'. Implements the average information algorithm as the main
tool to maximize the restricted log-likelihood, but with other algorithms
available.

Adaptive Group-Regularized Logistic Elastic Net Regression
Allows the user to incorporate multiple sources of co-data
(e.g., 	previously obtained p-values, published gene lists, and annotation) in the	estimation of a logistic regression model to enhance predictive performance and 	feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel 	(2018) <arXiv:1805.00389>.

rms (>= 4.1-3), lattice, latticeExtra, Formula, survival
The greport package contains many functions useful for
monitoring and reporting the results of clinical trials and other
experiments in which treatments are compared.  LaTeX is
used to typeset the resulting reports, recommended to be in the
context of knitr. The Hmisc and lattice packages are used by greport
for high-level graphics.