pp-eigentest

Posterior predictive eigenvalue testing for signal rank determination. Three-layer consensus architecture with FWER and FDR control; NumPy and JAX backends.

pp-eigentest is a posterior predictive eigenvalue testing framework for determining signal rank in high-dimensional datasets. It uses an INID bootstrap over Gram spectra as the null model and tests eigenvalue ratios via a three-layer consensus architecture:

  1. Dimensionality heuristics — parallel analysis variants, adjacent ratio/gap statistics, calibrated thresholds
  2. Adaptive thresholding — data-driven cutoffs that adjust to spectral structure
  3. Multiple testing correction — fixed-sequence FWER, Holm step-down, Benjamini–Hochberg FDR

Supports NumPy and JAX (GPU-accelerated) backends. Designed to consume posterior outputs from vbpca-py directly.

In development; pre-release. Source at jcm-sci/pp-eigentest.