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stochvol alternatives

The best stochvol alternatives in developer tools, ranked by Sparkpulse's velocity_score.

Updated Aug 20, 2026

Looking for the best alternatives to stochvol? Sparkpulse tracks and ranks 12 alternatives in developer tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, stochvol 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 stochvol

A Bayesian volatility sampler in its maintenance decade, paying for its own speed

stochvol runs MCMC for stochastic volatility models, with a C++ sampler underneath an R interface. Five years of releases in this window contain no new models: the work is compiler and dependency compatibility, CRAN check notes, and a steady trickle of corrections to the sampler itself. Its methodological milestone, the Journal of Statistical Software paper, is recorded in a 2021 tag.

Velocity 0.0 · Last update 47m ago

Read the full stochvol trajectory →

Top 12 alternatives to stochvol

Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.

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stochvol 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.

ProductVelocitySparks · 30dFocus areasLatest release
stochvol (baseline)0.00bayesian-inferencestochastic-volatilitymcmc
dscore0.00child-developmentpsychometricsglobal-healthdscore 2.0.0
ggfootball0.00sports-analyticsr-packagedata-scrapingggfootball 0.3.0
goat0.00bioinformaticsgene-set-analysisr-packageGene sets beyond human, and analyses you can reload
rollupTree0.00recursive-computationtree-structuresdag
massProps0.00systems-engineeringmass-propertiesuncertainty-propagation
writeAlizer0.00writing-assessmentnlp-featuresmodel-artifacts
samplr0.00cognitive-sciencesampling-algorithmsmcmc
midr0.00explainable-aisurrogate-modelsshapleyMatrix responses bring multi-class and survival models in scope
GeoThinneR0.00spatial-thinningspecies-distributionoccurrence-dataGeoThinned result objects and a reorganised method surface
selection.index0.00plant-breedingselection-indexgenomic-selectionGenomic, marker and multi-stage selection indices on an Rcpp core
packageRank0.00crandownload-analyticslog-filtering
melodi0.00official-statisticsapi-clientinsee

The 12 best stochvol alternatives, in depth

1. dscore · velocity 0.0

The D-score reference implementation rebuilt its measurement foundation on seven countries.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “dscore 2.0.0”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, dscore focuses on child development, psychometrics and global health.

dscore and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

2. ggfootball · velocity 0.0

A football-viz package just swapped scraping for an API and broke its own output to do it.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “ggfootball 0.3.0”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, ggfootball focuses on sports analytics, r package and data scraping.

ggfootball and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

3. goat · velocity 0.0

A gene-set enrichment package that outgrew its human-only origins, then went quiet.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Gene sets beyond human, and analyses you can reload”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, goat focuses on bioinformatics, gene set analysis and r package.

goat and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

4. rollupTree · velocity 0.0

The recursive-computation engine under massProps grows the accessors its consumer needed.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, rollupTree focuses on recursive computation, tree structures and dag.

rollupTree and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

5. massProps · velocity 0.0

A mass-properties rollup spends a year on documentation and follows its sibling's API.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, massProps focuses on systems engineering, mass properties and uncertainty propagation.

massProps and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

6. writeAlizer · velocity 0.0

Six months of releases and not one of them touched the scoring models.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, writeAlizer focuses on writing assessment, nlp features and model artifacts.

writeAlizer and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

7. samplr · velocity 0.0

A cognitive-science sampling package ships once, then goes quiet for eighteen months.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, samplr focuses on cognitive science, sampling algorithms and mcmc.

samplr and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

8. midr · velocity 0.0

A black-box interpreter reaches CRAN, then learns multi-class and survival responses.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Matrix responses bring multi-class and survival models in scope”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, midr focuses on explainable ai, surrogate models and shapley.

midr and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

9. GeoThinneR · velocity 0.0

Spatial thinning grows a result object, and the API breaks to make room for it.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “GeoThinned result objects and a reorganised method surface”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, GeoThinneR focuses on spatial thinning, species distribution and occurrence data.

GeoThinneR and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

10. selection.index · velocity 0.0

A dormant plant-breeding package returns as a genomic selection index suite.

Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Genomic, marker and multi-stage selection indices on an Rcpp core”.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, selection.index focuses on plant breeding, selection index and genomic selection.

selection.index and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

11. packageRank · velocity 0.0

CRAN download analytics maintained one micro-change at a time, hundreds per year.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, packageRank focuses on cran, download analytics and log filtering.

packageRank and stochvol have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.

12. melodi · velocity 0.0

INSEE's statistics API gets a French R client that keeps meeting its edge cases.

Its velocity score of 0.0/10 reflects longer-term release cadence.

Where stochvol leans on bayesian inference, stochastic volatility and mcmc, melodi focuses on official statistics, api client and insee.

melodi and stochvol 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 stochvol?

The top stochvol alternatives we currently track in developer tools are dscore, ggfootball, goat, rollupTree, massProps, ranked by recent ship velocity.

How is this list of stochvol alternatives ranked?

Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.

Can I compare stochvol directly with one of these alternatives?

Yes — every card has a "Compare with stochvol" link to a side-by-side /compare page.