R Binding for the 'Certifiably Optimal RulE ListS (Corels)'
Learner
The 'Certifiably Optimal RulE ListS (Corels)' learner by
Angelino et al described in <arXiv:1704.01701> provides interpretable decision
rules with an optimality guarantee, and is made available to R with this package.
See the file 'AUTHORS' for a list of copyright holders and contributors.

Navin Loganathan [aut],
Mohan Manivannan [aut],
Santhosh Sasanapuri [aut, cre],
LatentView Analytics [ctb]
R (>= 3.0.1), ggplot2, gplots, RColorBrewer, igraph, stats,
grDevices
Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features
using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables
using data transformations.

Record Linkage, Incorporating Imputation for Missing Agreement
Patterns, and Modeling Correlation Patterns Between Fields
A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.

Count Regression for Correlated Observations with the
Beta-Binomial
c(
person("Bryan D", "Martin", email = "bmartin6@uw.edu", role = c("aut", "cre")),
person("Daniela", "Witten", email = "dwitten@uw.edu", role = "aut"),
person("Amy D", "Willis", email = "adwillis@uw.edu", role = "aut")
)
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
stats, utils, optimr, VGAM, numDeriv, Matrix, ggplot2, trust,
dplyr, magrittr, rmutil, detectseparation, scales, phyloseq
knitr, rmarkdown, testthat, covr, limma, R.rsp
Bryan D Martin [aut, cre],
Daniela Witten [aut],
Amy D Willis [aut]