Posts tagged #pca

PandRS 0.4.0 Released — Where the DataFrame Finally Computes the Right Answer

The high-performance Pure Rust DataFrame library reaches 0.4.0 — a correctness landmark. PandRS replaces a large swath of fabricated and stubbed ML and statistical results with real algorithms: Jacobi-rotation PCA, t-SNE, DBSCAN, agglomerative clustering, IRLS logistic regression, isolation-forest/LOF/OneClassSVM anomaly detection, real silhouette and ROC-AUC, and genuine chi-square/t/F p-values. SciRS2-Core becomes a non-optional core dependency, and the SciRS2 stats/linalg integration deepens onto the 0.5 line. The DataFrame layer of the COOLJAPAN scientific stack — no pandas, no scikit-learn C-extensions, no GIL.