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and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished).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.

Tests, utilities, and case studies for analyzing significance in
  clustered binary matched-pair data. The central function clust.bin.pair uses
  one of several tests to calculate a Chi-square statistic. Implemented are the
  tests Eliasziw (1991) <doi:10.1002/sim.4780101211>, Obuchowski (1998)
  <doi:10.1002/(SICI)1097-0258(19980715)17:13%3C1495::AID-SIM863%3E3.0.CO;2-I>,
  Durkalski (2003) <doi:10.1002/sim.1438>, and Yang (2010)
  <doi:10.1002/bimj.201000035> with McNemar (1947) <doi:10.1007/BF02295996>
  included for comparison. The utility functions nested.to.contingency and
  paired.to.contingency convert data between various useful formats. Thyroids
  and psychiatry are the canonical datasets from Obuchowski and Petryshen (1989)
  <doi:10.1016/0165-1781(89)90196-0> respectively.

Evaluates the stability and significance of clusters on 'igraph' graphs.
    Supports weighted and unweighted graphs. Implements the cluster evaluation methods
    defined by Arratia A, Renedo M (2021) <doi:10.7717/peerj-cs.600>. Also includes an
    implementation of the Reduced Mutual Information introduced by Newman et al. (2020) 
    <doi:10.1103/PhysRevE.101.042304>.

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 
    and method stability using element-centric clustering comparison (Gates et 
    al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package 
    enables stability-based parameter assessment for graph-based clustering 
    pipelines typical in single-cell data analysis.