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discoCVI 0.2.1 (2026-10-28)

New vignette, "Evaluating density-based clusterings with discoCVI", with a complete workflow: clustering with HDBSCAN, converting noise labels, choosing a hyperparameter with DISCO, and inspecting pointwise and noise scores (vignette("discoCVI")).

disco_samples() and disco_score() no longer build any n x n matrix. The minimum spanning tree of the mutual-reachability graph is computed in compiled code (Rcpp) with distances evaluated on the fly, and all quantities needed by DISCO (sums and minima of density-connectivity distances per cluster) are obtained in a single pass over the resulting single-linkage merge tree. Memory now grows linearly with the number of points (times the number of clusters) instead of quadratically. On synthetic benchmarks, 0.2.0 is 23 to 138 times faster than 0.1.1 (n = 500 to 4,000; 1,222 MB vs 93 MB peak memory at n = 4,000), and 100,000 points can be scored in about 50 seconds with about 200 MB peak memory for the whole R process.
extract_dc_distances_from_mst() fills the DC-distance matrix block by block in Kruskal order instead of running a breadth-first search from every point.
compute_dc_distances() no longer builds the mutual-reachability matrix.
p_cluster() is vectorised (one matrix product instead of nested loops).

Points that form a singleton cluster now receive a cluster score of 0, as defined in the DISCO paper (Beer et al., Section 3, "Edge cases") and as in scikit-learn's silhouette_samples(). Version 0.1.1 gave them a score of 1, which inflated disco_score() for labelings containing singleton clusters (for example from DBSCAN with a small minPts). Labelings without singleton clusters are not affected.

Apart from the singleton fix above, all results are identical to version 0.1.1 up to floating-point rounding. A test suite comparing every exported function against the 0.1.1 implementation has been added (tests/testthat).