Matrix Experiment

Computes correlation coefficients and confidence intervals and clusters them hierarchically. To enable high-performance analysis of large datasets, adjustable 2D windowing is used to compute correlations for visible variables only (if clustering is disabled) and render only those to the DOM (irrespective of clustering).

Variables marked with an asterix are log-transformed.