R: A Partial Clustering Algorithm

# A Partial Clustering Algorithm

## Documentation for package ‘CrossClustering’ version 4.1.3

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

ari | Computes the adjusted Rand index and the confidence interval, comparing two classifications from a contingency table.
cc_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_get_cluster.crossclustering | Provides the vector of clusters' ID to which each element belong to.
cc_get_cluster.default | 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
print.ari | Computes the adjusted Rand index and the confidence interval, comparing two classifications from a contingency table.
print.crossclustering | A partial clustering algorithm with automatic estimation of the number of clusters and identification of outliers
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