Function reference • diceR

Functions
`consensus_cluster()`	Consensus clustering
`consensus_combine()`	Combine algorithms
`consensus_evaluate()`	Evaluate, trim, and reweigh algorithms
`consensus_matrix()`	Consensus matrix
`graph_cdf()` `graph_delta_area()` `graph_heatmap()` `graph_tracking()` `graph_all()`	Graphical Displays
`ev_nmi()` `ev_confmat()`	External validity indices
`impute_knn()`	K-Nearest Neighbours imputation
`impute_missing()`	Impute missing values
`pcn_simulate()` `pcn_select()`	Simulate and select null distributions on empirical gene-gene correlations
`dice()`	Diverse Clustering Ensemble
`CSPA()`	Cluster-based Similarity Partitioning Algorithm (CSPA)
`k_modes()`	K-modes
`majority_voting()`	Majority voting
`LCE()`	Linkage Clustering Ensemble
`LCA()`	Latent Class Analysis
`PAC()`	Proportion of Ambiguous Clustering
`cts()` `srs()` `asrs()`	Similarity Matrices
`compactness()`	Compactness Measure
`prepare_data()`	Prepare data for consensus clustering
`relabel_class()`	Relabel classes to a standard
`min_fnorm()`	Minimize Frobenius norm for between two matrices
`sigclust()`	Significant Testing of Clustering Results
Data
`hgsc`	Gene expression data for High Grade Serous Carcinoma from TCGA
Package Documentation
`diceR-package`	diceR: Diverse Cluster Ensemble in R

Reference