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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.