fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of mmconvert and tglkmeans — release velocity, themes, recent moves, and the top alternatives to consider.
A single-purpose mouse map interpolator that solved its problem in 2023 and has coasted since
mmconvert does one thing: interpolate between GRCm39 physical positions and the revised Cox genetic map for mouse MUGA array markers. The substantive work all landed in a burst across 2021-2023 — the initial function, the GRCm39 annotation dataset, cross2_to_grcm39(), the recomputed Cox maps and their smoothed replacement. Everything since is upkeep: a warning-message fix in 0.12, and 0.14 is a test adjustment to silence a CRAN Note with no code change at all.
A k-means implementation that just told users their Spearman clustering on missing data was wrong
tglkmeans is a multi-core k-means implementation with seeding, aimed at single-cell and other large matrix workloads. Version 0.4.0 flipped the id_column default and moved to R's random number generator, 0.5.x added count-matrix downsampling and fixed id handling, and 0.6.3 in May 2026 is a correctness release: Spearman distance was ranking missing values as the largest value instead of dropping them, and predict_tgl_kmeans() with Euclidean distance did not reproduce the training metric when a cluster center had a missing dimension.
mmconvert does one thing: interpolate between GRCm39 physical positions and the revised Cox genetic map for mouse MUGA array markers. The substantive work all landed in a burst across 2021-2023 — the initial function, the GRCm39 annotation dataset, cross2_to_grcm39(), the recomputed Cox maps and their smoothed replacement. Everything since is upkeep: a warning-message fix in 0.12, and 0.14 is a test adjustment to silence a CRAN Note with no code change at all.
The package has reached the natural end state of a reference-data converter — the reference data stopped moving, so the package stopped moving. Releases now arrive roughly annually and exist to keep CRAN checks green. The 0.14 release shipped the same day as sibling qtl2convert 0.36, confirming these are batch maintenance passes across the maintainer's packages rather than independent development.
Without a new mouse genome build or a revised Cox map, the next release is likely another CRAN-check accommodation rather than new functionality.
tglkmeans is a multi-core k-means implementation with seeding, aimed at single-cell and other large matrix workloads. Version 0.4.0 flipped the id_column default and moved to R's random number generator, 0.5.x added count-matrix downsampling and fixed id handling, and 0.6.3 in May 2026 is a correctness release: Spearman distance was ranking missing values as the largest value instead of dropping them, and predict_tgl_kmeans() with Euclidean distance did not reproduce the training metric when a cluster center had a missing dimension.
The package handles missing data across three distance metrics, and 0.6.3 shows those paths had drifted apart — Spearman behaved unlike Euclidean and Pearson, and prediction behaved unlike training. Both fixes change results on affected data, and the release notes are careful to bound exactly where: Spearman on data with NAs changes, complete data does not. Performance work runs alongside, with the dense per-thread vote matrix removed from the reassignment step.
With the metric paths now aligned on missing-value handling, further work is more likely to target the parallel reassignment internals than the distance semantics.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either mmconvert or tglkmeans.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all mmconvert alternatives → · See all tglkmeans alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. mmconvert and tglkmeans are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mmconvert and tglkmeans are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top mmconvert alternatives in Analytics are ranked by recent ship velocity. Browse the "mmconvert alternatives" section above for the current picks, or visit /alternatives/mmconvert for the full list with editorial commentary on each.
Top tglkmeans alternatives in Analytics are ranked by recent ship velocity. Browse the "tglkmeans alternatives" section above for the current picks, or visit /alternatives/tglkmeans for the full list with editorial commentary on each.