Title: Gaussian-Based Visualization of Gaussian and Non-Gaussian
Description: 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.
Imports: Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod, VarSelLCM

Imports: Rcpp, MASS, parallel, mgcv, mvtnorm, Rmixmod, VarSelLCM (>=

Title: Simultaneous Detection of Clusters and Cluster-Specific Genes in
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'))
Description: 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and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).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.
Imports: stats (>= 3.5.0), utils (>= 3.5.0), VennDiagram, scales (>=

role = c("aut", "cre")), person("Irina", "Mohorianu",
Description: A set of tools for evaluating clustering similarity across methods
and method stability using element-centric clustering comparison (Gates et
enables data-wide assessment of clustering robustness using proportion of
ambiguously clustered pairs (Senbabaoglu et al. (2014)
<doi:10.1038/srep06207>), which can be used to infer the optimal number of
Imports: ggplot2, dplyr, fastcluster, rlang, Matrix, igraph, magrittr,
Suggests: knitr, rmarkdown, e1071, dbscan, dendextend, Seurat

role = c("aut", "cre")), person("Andi", "Munteanu",
Description: A set of tools for evaluating clustering robustness using
proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014)
<doi:10.1038/srep06207>), as well as similarity across methods
enables stability-based parameter assessment for graph-based clustering
pipelines typical in single-cell data analysis.
Rcpp, methods, stats, foreach, doParallel, irlba, progress,
Suggests: knitr, rmarkdown, e1071, dbscan, dendextend, Seurat, readr,