varimax

Maximum Covariance Analysis in Python

Factor Analysis Kit

Principle Component Analysis (PCA) with varimax rotation.

Hierarchical clustering and partitional clustering with exploratory factor analysis on chocolate quality.

Connectivity-based parcellation

Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.

Statistical properties of entromin as an orthogonal rotation criterion