Mutual information, not variance: fixing PCA's inflated rank estimates
Standard PCA's scree plot lies about noisy, mixed-type tabular data, shattering nonlinear dependencies into spurious orthogonal dimensions that inflate the true rank. Entropic Scree replaces variance with normalized mutual information to collapse that to the real generative rank, and even sizes autoencoder bottlenecks from it. The standalone R function is live with CRAN and PyPI packages on the way.