Performing diagnostics on binomial regression models
Rune Haubo B Christensen and Merete K Hansen
This package provides a range of diagnostic methods for
binomial regression models.

'Taxonomic' Specification and Parsing Methods
Includes functions for working with taxonomic data,
including functions for combining, separating, and filtering
taxonomic groups by any rank or name. Allows standard (SE)
and non-standard evaluation (NSE).
person("Scott", "Chamberlain", role = c("aut", "cre"),
email = "myrmecocystus@gmail.com")
methods, stats, jsonlite, lazyeval, dplyr

Includes functions for working with taxonomic data,
including functions for combining, separating, and filtering
taxonomic groups by any rank or name. Allows standard ('SE')
and non-standard evaluation ('NSE').
roxygen2 (>= 6.0.1), testthat, knitr, taxize, covr

Mixture Models for Binomial and Longitudinal Data
Faustine Bousquet <faustine.bousquet@umontpellier.fr>, Sophie Lèbre <sophie.lebre@umontpellier.fr>, Christian Lavergne <christian.lavergne@umontpellier.fr>
Provides a clustering method of non-gaussian longitudinal data with a mixture of generalized linear models. The longitudinal data should be defined as repeated observations for each individual. The number of observations for each individual can be different. In runEM(), an expectation-maximization algorithm is developed for both binomial and longitudinal data mixture model.
lubridate (>= 1.7.0), Rmpfr, MASS, gmp, dplyr, stringr, rlang,
stats
testthat, devtools, roxygen2, knitr, rmarkdown, ggplot2, qpdf

Binomial Random Forest Feature Selection
person("Samir", "Rachid Zaim", email = "samirrachidzaim@math.arizona.edu", role = c("aut", "cre"))
The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
Software, GenePrediction, StatisticalMethod, DecisionTree,
DimensionReduction, ExperimentalDesign
randomForest, ggplot2, data.table, stats, rlist, correlbinom,
parallel,Rmpfr, methods