CCtestr provides a hypothesis testing framework for cluster validation. Using a modified consensus clustering algorithm, PAC scores are calculated for data simulated from an input matrix in order to estimate the theoretical null distribution of each cluster number k's performance metrics. Results for real data are subsequently evaluated relative to these distributions. By systematically testing against well-formulated null hypotheses, test statistics and p-values are computed with high accuracy and known precision.
|License||GPL (>= 3)|
|Package repository||View on GitHub|
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