Combined Cluster and Discriminant Analysis
Jozsef Kovacs, Solt Kovacs, Norbert Magyar, Peter Tanos, Istvan Gabor Hatvani, Angela Anda(2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010

Implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. <doi:10.1016/j.envsoft.2014.01.010>.

This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. DOI: http://dx.doi.org/10.1016/j.envsoft.2014.01.010

Distribution-Free Single-Cell Differential Expression Analysis
c(person("Marine", "Gauthier", role = c("aut","cre"),
email = "marine.gauthier@u-bordeaux.fr"),
person("Denis", "Agniel", role = c("aut"),
email = "denis.agniel@gmail.com"),
person("Boris P.", "Hejblum", role = c("aut"),
email = "boris.hejblum@u-bordeaux.fr"))
pbapply,parallel,matrixStats,CompQuadForm, RcppNumerical,
doParallel, foreach, ggplot2, randomForest, rpart, statmod,
viridisLite, survey, cowplot
Complex hypothesis testing through conditional cumulative distribution function estimation.
Method is detailed in: Gauthier M, Agniel D, Thiebaut R & Hejblum BP (2020).
"Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis", BioRxiv <doi:10.1101/2021.05.21.445165>.
Marine Gauthier [aut, cre],
Denis Agniel [aut],
Boris P. Hejblum [aut]

CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks
person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre"))
Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) <http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.