Initial CRAN-targeted release. Native R port of the Python spqrp
package.
run_clustering()) -- kNN graph in a
PCA/UMAP/MDS embedding, optional iterative split of large
components, ggplot visualization with patient-hue colouring and
legend.perform_distance_evaluation_on_ranked_proteins())
-- pairwise sample classification from a percentile cutoff on
pairwise distances, with FN/FP/percentile-overlay histogram.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.remove_outlier_samples())
-- pure-R via the solitude package; default outlier_threshold
calibrated empirically for solitude's anomaly-score scale.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.
articles/numerical-divergence.md for known cross-language
divergences (UMAP, random-forest backends, isolation-forest
scales, MDS solvers) and recommendations for cross-language
comparison.vignette("spqrp-mock-data") for a worked example on a small
bundled cohort.