ANALYTICS
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain
Alternatives · analytics tools
fastglm alternatives
The best fastglm alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 17, 2026
Looking for the best alternatives to fastglm? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, fastglm 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 fastglm
A fast GLM solver stops being one function and becomes a count-model family
fastglm ran C++ IRLS for standard generalized linear models for six years with almost no releases. In May 2026 it added three top-level model types — negative binomial with jointly estimated dispersion, hurdle, and zero-inflated — each with the entire fitting driver in C++ rather than an R loop around a C++ kernel. The following release generalised Firth bias reduction to every standard family across dense, sparse and streaming backends.
Velocity 0.0 · Last update 1h ago
Top 12 alternatives to fastglm
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
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A single-cell data store commits to Zarr v3 and range-readable hosting across four language surfaces
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A trend-extraction toolkit grows a full decomposition engine, seasonal components and all.
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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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Text analysis in R keeps optimising its token internals — and builds a path out to torch
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The ModernDive teaching package learns to render inside the browser that runs its own textbook
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The R client for PurpleAir sensors keeps finding its time-averaging was wrong.
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A board game graphics package runs one of the most disciplined deprecation cycles in R.
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The explainable-ensemble-tree package now measures whether its own explanations are faithful.
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The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
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A scientific-text analysis package moved from counting citations to classifying argument structure.
fastglm 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 |
|---|---|---|---|---|
| fastglm (baseline) | 0.0 | 0 | statistical-computinggeneralized-linear-modelscpp | CRAN release 0.1.0 |
| glmbayes | 6.3 | 1 | bayesian-statisticsgeneralized-linear-modelsopencl | OpenCL split out to nmathopencl; insight and bayestestR integration |
| lstar | 5.0 | 0 | single-cell-genomicszarrwasm | lstar 0.2.0 |
| 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 | — |
| quanteda | 2.5 | 0 | text-analysisnatural-language-processingr-package | CRAN v4.0 |
| moderndive | 2.5 | 0 | statistics-educationwebrregression | — |
| PurpleAir | 0.0 | 0 | air-qualitysensor-datar-package | — |
| piecepackr | 0.0 | 0 | board-gamesgraphicsr-package | — |
| e2tree | 0.0 | 0 | explainable-aiensemble-methodsdecision-trees | A significance-tested measure of explanation fidelity |
| discretefdr | 0.0 | 0 | multiple-testingfalse-discovery-ratediscrete-statistics | — |
| contentanalysis | 0.0 | 0 | text-analysisbibliometricsscientific-writing | Sentence-level rhetorical move classification arrives |
The 12 best fastglm alternatives, in depth
1. glmbayes · velocity 6.3
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain.
Over the last 30 days glmbayes shipped 1 meaningful update vs fastglm's 0, most recently “OpenCL split out to nmathopencl; insight and bayestestR integration”. Its velocity score of 6.3/10 blends that with longer-term release cadence.
Where fastglm leans on statistical computing, generalized linear models and cpp, glmbayes focuses on bayesian statistics, generalized linear models and opencl.
Over the last 30 days glmbayes has been shipping faster than fastglm — a point in its favour if release momentum matters to you.
2. lstar · velocity 5.0
A single-cell data store commits to Zarr v3 and range-readable hosting across four language surfaces.
Its velocity score of 5.0/10 reflects longer-term release cadence; its most recent meaningful update was “lstar 0.2.0”.
Where fastglm leans on statistical computing, generalized linear models and cpp, lstar focuses on single cell genomics, zarr and wasm.
lstar and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. 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 fastglm'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 fastglm leans on statistical computing, generalized linear models and cpp, trendseries focuses on time series, econometrics and r package.
Over the last 30 days trendseries has been shipping faster than fastglm — a point in its favour if release momentum matters to you.
Full trendseries trajectory → · Compare fastglm vs trendseries →
4. 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 fastglm leans on statistical computing, generalized linear models and cpp, qtl2 focuses on qtl mapping, statistical genetics and bioinformatics.
qtl2 and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
5. 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 fastglm leans on statistical computing, generalized linear models and cpp, r-owidapi focuses on open data, our world in data and r package.
r-owidapi and fastglm 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 fastglm vs r-owidapi →
6. quanteda · velocity 2.5
Text analysis in R keeps optimising its token internals — and builds a path out to torch.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “CRAN v4.0”.
Where fastglm leans on statistical computing, generalized linear models and cpp, quanteda focuses on text analysis, natural language processing and r package.
quanteda and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
7. moderndive · velocity 2.5
The ModernDive teaching package learns to render inside the browser that runs its own textbook.
Its velocity score of 2.5/10 reflects longer-term release cadence.
Where fastglm leans on statistical computing, generalized linear models and cpp, moderndive focuses on statistics education, webr and regression.
moderndive and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full moderndive trajectory → · Compare fastglm vs moderndive →
8. PurpleAir · velocity 0.0
The R client for PurpleAir sensors keeps finding its time-averaging was wrong.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where fastglm leans on statistical computing, generalized linear models and cpp, PurpleAir focuses on air quality, sensor data and r package.
PurpleAir and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full PurpleAir trajectory → · Compare fastglm vs PurpleAir →
9. piecepackr · velocity 0.0
A board game graphics package runs one of the most disciplined deprecation cycles in R.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where fastglm leans on statistical computing, generalized linear models and cpp, piecepackr focuses on board games, graphics and r package.
piecepackr and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full piecepackr trajectory → · Compare fastglm vs piecepackr →
10. e2tree · velocity 0.0
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “A significance-tested measure of explanation fidelity”.
Where fastglm leans on statistical computing, generalized linear models and cpp, e2tree focuses on explainable ai, ensemble methods and decision trees.
e2tree and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
11. discretefdr · velocity 0.0
The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where fastglm leans on statistical computing, generalized linear models and cpp, discretefdr focuses on multiple testing, false discovery rate and discrete statistics.
discretefdr and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full discretefdr trajectory → · Compare fastglm vs discretefdr →
12. contentanalysis · velocity 0.0
A scientific-text analysis package moved from counting citations to classifying argument structure.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Sentence-level rhetorical move classification arrives”.
Where fastglm leans on statistical computing, generalized linear models and cpp, contentanalysis focuses on text analysis, bibliometrics and scientific writing.
contentanalysis and fastglm have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full contentanalysis trajectory → · Compare fastglm vs contentanalysis →
Frequently asked questions
What are the best alternatives to fastglm?
The top fastglm alternatives we currently track in analytics tools are glmbayes, lstar, trendseries, qtl2, r-owidapi, ranked by recent ship velocity.
How is this list of fastglm alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare fastglm directly with one of these alternatives?
Yes — every card has a "Compare with fastglm" link to a side-by-side /compare page.