Function reference • CrossClustering

All functions
`ari()` `print(<ari>)`	Computes the adjusted Rand index and the confidence interval, comparing two classifications from a contingency table.
`cc_crossclustering()` `print(<crossclustering>)`	A partial clustering algorithm with automatic estimation of the number of clusters and identification of outliers
`cc_get_cluster()`	Provides the vector of clusters' ID to which each element belong to.
`cc_test_ari()`	A test for testing the null hypothesis of random agreement (i.e., adjusted Rand Index equal to 0) between two partitions.
`cc_test_ari_permutation()`	A permutation test for testing the null hypothesis of random agreement (i.e., adjusted Rand Index equal to 0) between two partitions.
`chain_effect`	A toy dataset for illustrating the chain effect.
`consensus_cluster()`	Get clusters which reach max consensus
`is_zero()`	Check for zero
`nb_data`	RNA-Seq dataset example
`prune_zero_tail()`	Prune tail made of zeros
`reverse_table()`	Reverse the process of create a contingency table
`toy`	A toy example matrix
`twomoons`	A famous shape data set containing two clusters with two moons shapes and outliers
`worms`	A famous shape data set containing two clusters with two worms shapes and outliers

Reference