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Comparison · Analytics
robma vs tglkmeans
A side-by-side editorial comparison of robma and tglkmeans — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
robma vs tglkmeans: at a glance
What is robma?
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
What is tglkmeans?
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.
robma vs tglkmeans: editorial side-by-side
robma
0.0ANALYTICS
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
◆ Current state
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
◆ Where it's heading
The 3.x series solved the modeling problem and left an interface problem behind: a caller had to know which of six constructors matched their data type, and argument names differed across them. 4.0.0 resolves that by making the model family a set of arguments rather than a function name, and by standardizing input naming on metafor-style conventions. It shipped one day after BayesTools 0.3.0, the author's own upstream infrastructure package, whose new standardization and prior-transformation machinery this rewrite depends on.
◆ Prediction
A rewrite this wide usually needs a follow-up, so expect 4.0.x patches addressing migration gaps as users hit the removed constructors and renamed arguments.
tglkmeans
0.0ANALYTICS
A k-means implementation that just told users their Spearman clustering on missing data was wrong
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
Alternatives to robma and tglkmeans
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 robma or tglkmeans.
gcube
gcube's recent releases are all packaging metadata, not simulation code
cubist
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats
ggstats keeps widening what a coefficient or Likert plot can be
ecodive
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
simStateSpace
State-space data simulation for R, filled in one function at a time
See all robma alternatives → · See all tglkmeans alternatives →
Recent activity from robma and tglkmeans
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agotglkmeansSpearman metric no longer ranks missing values as the largest
- 3mo agorobmaUnifies six model constructors into one brma class hierarchy ⚡
- 8mo agorobmaRoBMA 3.6.1
- 11mo agorobmaRoBMA 3.6.0
- 1y agorobmaRoBMA 3.5.1
- 1y agorobmaRoBMA 3.5.0
- 1y agorobmaRoBMA 3.4.0
- 2y agotglkmeansFixes corrupted cluster ids and dropped dimnames
- 2y agotglkmeansAdds downsample_matrix() for count matrices
- 2y agotglkmeansBreaking: id_column defaults to FALSE, switches to R's RNG
Frequently asked questions
What is the difference between robma and tglkmeans?
Both compete on the same themes — r-package — within Analytics. robma 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.
Is robma better than tglkmeans?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. robma 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.
What are the best alternatives to robma?
Top robma alternatives in Analytics are ranked by recent ship velocity. Browse the "robma alternatives" section above for the current picks, or visit /alternatives/robma for the full list with editorial commentary on each.
What are the best alternatives to tglkmeans?
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.