BAMBI, igraph, purrr, ggplot2, rlang, stats, utils

Prediction and Clustering on the Torus by Conformal Prediction

BAMBI, igraph, purrr, ggplot2, rlang, stats, utils, cowplot

Gaussian-Based Visualization of Generic Model-Based Clustering
c(person("Christophe", "Biernacki", email = "christophe.biernacki@math.univ-lille1.fr", role = c("aut")), person("Matthieu", "Marbac", email = "matthieu.marbac@gmail.com", role = c("aut", "cre")), person("Vincent", "Vandewalle", email = "vincent.vandewalle@inria.fr", role = c("aut")))
Christophe Biernacki [aut],
Matthieu Marbac [aut, cre],
Vincent Vandewalle [aut]
Gaussian-Based Visualization of clustering achieved by mixture model on any type of data. Visualization is based on the probabilities of classification.
Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod
'clusvis.R' 'estimation.R' 'smartinit.R' 'RcppExports.R'
'plot.R' 'clusvismixmod.R' 'modessearch.R'

Gaussian-Based Visualization of Gaussian and Non-Gaussian
Model-Based Clustering
Gaussian-Based Visualization of Gaussian and Non-Gaussian Model-Based Clustering done on any type of data. Visualization is based on the probabilities of classification.
Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod, VarSelLCM
'clusvis.R' 'estimation.R' 'smartinit.R' 'RcppExports.R'
'plot.R' 'clusvismixmod.R' 'clusvisvarsellcm.R' 'modessearch.R'

Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod, VarSelLCM (>=
2.1)

Simultaneous Detection of Clusters and Cluster-Specific Genes in
High-Throughput Transcriptome Data
c(
person("Li", "Jun", email = "jun.li@nd.edu", role = "cre"),
person("Jiang", "Ge", email = "gejiang2@illinois.edu", role = "aut"),
person("Wang", "Chuanqi", email = "cwang14@nd.edu", role = 'ctb'))
Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).
Simultaneously perform clustering analysis and signature 	gene selection on high-dimensional transcriptome data sets. 	To do so, 'ClussCluster' incorporates a Lasso-type 	regularization penalty term to the objective function of K-	means so that cell-type-specific signature genes can be 	identified while clustering the cells.
Li Jun [cre],
Jiang Ge [aut],
Wang Chuanqi [ctb]