Title: Confidence Intervals and Sample Size Determination for a
Binomial Proportion under Simple Random Sampling and Pooled
Author: Michael H�hle [aut, cre], Wei Liu [ctb]

Title: Performing diagnostics on binomial regression models
Author: Rune Haubo B Christensen and Merete K Hansen
Maintainer: Merete K Hansen <mkh@imm.dtu.dk>
Description: This package provides a range of diagnostic methods for

Title: 'Taxonomic' Specification and Parsing Methods
Description: Includes functions for working with taxonomic data,
including functions for combining, separating, and filtering
taxonomic groups by any rank or name. Allows standard (SE)
Authors@R: person("Scott", "Chamberlain", role = c("aut", "cre"),
Imports: methods, stats, jsonlite, lazyeval, dplyr

taxonomic groups by any rank or name. Allows standard ('SE')
Suggests: roxygen2 (>= 6.0.1), testthat, knitr, taxize, covr

Title: Mixture Models for Binomial and Longitudinal Data
Author: Faustine Bousquet <faustine.bousquet@umontpellier.fr>, Sophie Lèbre <sophie.lebre@umontpellier.fr>, Christian Lavergne <christian.lavergne@umontpellier.fr>
Description: 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.
Imports: lubridate (>= 1.7.0), Rmpfr, MASS, gmp, dplyr, stringr, rlang,
Suggests: testthat, devtools, roxygen2, knitr, rmarkdown, ggplot2, qpdf

Title: Binomial Random Forest Feature Selection
Authors@R: person("Samir", "Rachid Zaim", email = "samirrachidzaim@math.arizona.edu", role = c("aut", "cre"))
Description: 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.
biocViews: Software, GenePrediction, StatisticalMethod, DecisionTree,
Imports: randomForest, ggplot2, data.table, stats, rlist, correlbinom,
Packaged: 2020-01-20 15:57:55 UTC; samirrachidzaim-admin
Maintainer: Samir Rachid Zaim <samirrachidzaim@math.arizona.edu>