Title: Combined Cluster and Discriminant Analysis
Author: Solt Kovacs, Jozsef Kovacs, Peter Tanos
Description: 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

Description: 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>.

Description: 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

Title: Distribution-Free Single-Cell Differential Expression Analysis
Authors@R: c(person("Marine", "Gauthier", role = c("aut","cre"),
person("Denis", "Agniel", role = c("aut"),
person("Boris P.", "Hejblum", role = c("aut"),
doParallel, foreach, ggplot2, randomForest, rpart, statmod,
Description: 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>.
Packaged: 2021-09-24 05:07:14 UTC; marinegauthier

Title: CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks
Authors@R: person("Bryon", "Aragam", email = "sparsebn@gmail.com", role = c("aut", "cre"))
Description: 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.
Imports: sparsebnUtils, Rcpp (>= 0.11.0)