Changes in version 0.1.0 (2026-06-17)                  

Initial CRAN-targeted release. Native R port of the Python spqrp
package.

Features

  - Clustering pipeline (run_clustering()) -- kNN graph in a
    PCA/UMAP/MDS embedding, optional iterative split of large
    components, ggplot visualization with patient-hue colouring and
    legend.
  - Threshold-based evaluation
    (perform_distance_evaluation_on_ranked_proteins()) -- pairwise
    sample classification from a percentile cutoff on pairwise
    distances, with FN/FP/percentile-overlay histogram.
  - Protein ranking (train_with_normalise()) -- pairwise random- forest
    classifier with three selectable backends; randomForest is the
    default (closest behaviour to Python's
    imblearn.BalancedRandomForestClassifier). Importance values are
    normalised to sum to 1.0, matching sklearn's
    clf.feature_importances_ convention.
  - Isolation-forest outlier filtering (remove_outlier_samples()) --
    pure-R via the solitude package; default outlier_threshold
    calibrated empirically for solitude's anomaly-score scale.

Verbosity

All functions are silent by default. Pass quiet = FALSE to any function
that emits status output to see progress messages, per-call summaries,
save-path hints, and cluster listings. Warnings about genuine data
issues -- e.g. samples dropped from analysis -- fire regardless of
quiet.

Documentation

  - See articles/numerical-divergence.md for known cross-language
    divergences (UMAP, random-forest backends, isolation-forest scales,
    MDS solvers) and recommendations for cross-language comparison.
  - See vignette("spqrp-mock-data") for a worked example on a small
    bundled cohort.