ANALYTICS
A trend-extraction toolkit grows a full decomposition engine, seasonal components and all.
Alternatives · analytics tools
tglkmeans alternatives
The best tglkmeans alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 17, 2026
Looking for the best alternatives to tglkmeans? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, tglkmeans shipped 0 meaningful updates in the last 30 days and carries a velocity score of 0.0 out of 10 in 2026. The alternatives below are ranked the same way, so you're comparing real release momentum, not marketing claims.
About 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.
Velocity 0.0 · Last update 48m ago
Top 12 alternatives to tglkmeans
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
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The standard QTL mapping package in R opened its genome scan to user-supplied likelihood models.
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The R client for Our World in Data found its search had been reading a tenth of the catalog.
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The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
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Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
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A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
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A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
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A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
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The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
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RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
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A customer segmentation package that went quiet for six years and returned with dependency hygiene
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A counterfactual estimator turning itself into a platform for multiple estimands
tglkmeans vs alternatives — shipping velocity at a glance
Velocity score (0–10) and meaningful releases shipped in the last 30 days, from official changelogs. Higher = shipping faster.
| Product | Velocity | Sparks · 30d | Focus areas | Latest release |
|---|---|---|---|---|
| tglkmeans (baseline) | 0.0 | 0 | r-packageclusteringmissing-data | — |
| trendseries | 3.8 | 1 | time-serieseconometricsr-package | Decomposition becomes a first-class operation, five methods deep |
| qtl2 | 2.5 | 0 | qtl-mappingstatistical-geneticsbioinformatics | A genome scan that takes your own likelihood function |
| r-owidapi | 2.5 | 0 | open-dataour-world-in-datar-package | — |
| nflreadr | 0.0 | 0 | r-packagesports-analyticsdata-access | — |
| susier | 0.0 | 0 | r-packagestatistical-geneticsfine-mapping | — |
| UCell | 0.0 | 0 | r-packagesingle-cellgene-signatures | — |
| detectseparation | 0.0 | 0 | r-packageregressiondiagnostics | — |
| brglm2 | 0.0 | 0 | r-packageregressionbias-reduction | 1.0.0 adds maximum DY-prior penalized likelihood for logistic regression |
| bayestools | 0.0 | 0 | r-packagebayesianjags | — |
| robma | 0.0 | 0 | r-packagemeta-analysisbayesian | Unifies six model constructors into one brma class hierarchy |
| rfm | 0.0 | 0 | r-packagecustomer-analyticssegmentation | — |
| fect | 0.0 | 0 | r-packagecausal-inferencepanel-data | Post-hoc estimand API decouples estimands from the fit |
The 12 best tglkmeans alternatives, in depth
1. trendseries · velocity 3.8
A trend-extraction toolkit grows a full decomposition engine, seasonal components and all.
Over the last 30 days trendseries shipped 1 meaningful update vs tglkmeans's 0, most recently “Decomposition becomes a first-class operation, five methods deep”. Its velocity score of 3.8/10 blends that with longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, trendseries focuses on time series, econometrics and r package.
Over the last 30 days trendseries has been shipping faster than tglkmeans — a point in its favour if release momentum matters to you.
Full trendseries trajectory → · Compare tglkmeans vs trendseries →
2. qtl2 · velocity 2.5
The standard QTL mapping package in R opened its genome scan to user-supplied likelihood models.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “A genome scan that takes your own likelihood function”.
Where tglkmeans leans on r package, clustering and missing data, qtl2 focuses on qtl mapping, statistical genetics and bioinformatics.
qtl2 and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. r-owidapi · velocity 2.5
The R client for Our World in Data found its search had been reading a tenth of the catalog.
Its velocity score of 2.5/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, r-owidapi focuses on open data, our world in data and r package.
r-owidapi and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full r-owidapi trajectory → · Compare tglkmeans vs r-owidapi →
4. nflreadr · velocity 0.0
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, nflreadr focuses on r package, sports analytics and data access.
nflreadr and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full nflreadr trajectory → · Compare tglkmeans vs nflreadr →
5. susier · velocity 0.0
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, susier focuses on r package, statistical genetics and fine mapping.
susier and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
6. UCell · velocity 0.0
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, UCell focuses on r package, single cell and gene signatures.
UCell and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
7. detectseparation · velocity 0.0
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, detectseparation focuses on r package, regression and diagnostics.
detectseparation and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full detectseparation trajectory → · Compare tglkmeans vs detectseparation →
8. brglm2 · velocity 0.0
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “1.0.0 adds maximum DY-prior penalized likelihood for logistic regression”.
Where tglkmeans leans on r package, clustering and missing data, brglm2 focuses on r package, regression and bias reduction.
brglm2 and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
9. bayestools · velocity 0.0
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, bayestools focuses on r package, bayesian and jags.
bayestools and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full bayestools trajectory → · Compare tglkmeans vs bayestools →
10. robma · velocity 0.0
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Unifies six model constructors into one brma class hierarchy”.
Where tglkmeans leans on r package, clustering and missing data, robma focuses on r package, meta analysis and bayesian.
robma and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
11. rfm · velocity 0.0
A customer segmentation package that went quiet for six years and returned with dependency hygiene.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where tglkmeans leans on r package, clustering and missing data, rfm focuses on r package, customer analytics and segmentation.
rfm and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
12. fect · velocity 0.0
A counterfactual estimator turning itself into a platform for multiple estimands.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Post-hoc estimand API decouples estimands from the fit”.
Where tglkmeans leans on r package, clustering and missing data, fect focuses on r package, causal inference and panel data.
fect and tglkmeans have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Frequently asked questions
What are the best alternatives to tglkmeans?
The top tglkmeans alternatives we currently track in analytics tools are trendseries, qtl2, r-owidapi, nflreadr, susier, ranked by recent ship velocity.
How is this list of tglkmeans alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare tglkmeans directly with one of these alternatives?
Yes — every card has a "Compare with tglkmeans" link to a side-by-side /compare page.