Extract 'REDCap' Databases into Tidy 'Tibble's
c(
person("Richard", "Hanna", , "richardshanna91@gmail.com", role = c("aut", "cre")),
person("Stephan", "Kadauke", , "kadaukes@chop.edu", role = "aut")
)
Convert 'REDCap' exports into tidy tables for easy handling of 'REDCap' repeat instruments and event arms.
checkmate, dplyr, purrr, REDCapR (>= 1.1.0), rlang, stringi,
stringr, tibble, tidyr, tidyselect
Richard Hanna [aut, cre],
Stephan Kadauke [aut]

c(
person("Richard", "Hanna", , "richardshanna91@gmail.com", role = c("aut", "cre")),
person("Stephan", "Kadauke", , "kadaukes@chop.edu", role = "aut"),
person("Ezra", "Porter", , "ezrajporter@gmail.com", role = "aut")
)
covr, knitr, rmarkdown, testthat (>= 3.0.0)
Richard Hanna [aut, cre],
Stephan Kadauke [aut],
Ezra Porter [aut]

Regression Trees with Random Effects for Longitudinal (Panel)
Data
This package estimates regression trees with random
effects as a way to use data mining techniques to describe
longitudinal or panel data.

Rebecca Sela, Jeffrey Simonoff and Wenbo Jing
A data mining approach for longitudinal and clustered data,
which combines the structure of mixed effects model with tree-based
estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM
trees: a data mining approach for longitudinal and clustered data
<doi:10.1007/s10994-011-5258-3>.

REGENT: Risk Estimation for Genetic and Environmental Traits
Daniel J.M. Crouch, Graham Goddard H.M. & Cathryn M. Lewis
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information

Produces population distribution of disease risk and
statistical risk categories, and predicts risks for individuals
with genotype information

Risk Estimation for Genetic and Environmental Traits
Daniel J.M. Crouch, Graham H.M. Goddard & Cathryn M. Lewis
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.

Random Effects for the Identification of Differential Splicing
Contains the REIDS
model presented in Van Moerbeke et al
(2017) <doi:10.1186/s12859-017-1687-8> for the detection of alternative splicing.  The method is extended by incorporating junction information for the
assessment of alternative splicing. The vignette introduces the model and shows an example work flow.
R (>= 2.10), aroma.affymetrix, aroma.core