ANALYTICS
mlr3mbo picked its defaults from a benchmark study, not from taste
Alternatives · analytics tools
feasts alternatives
The best feasts alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 13, 2026
Looking for the best alternatives to feasts? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, feasts 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 feasts
feasts is splitting itself in two, moving every plot into ggtime
feasts provides feature extraction and statistics for tsibble time series. The last two releases are dominated by one decision: all of its gg_*() plotting functions are being moved out into a separate ggtime package. 0.4.2 announced the deprecation and 0.5.0 makes ggtime a dependency with soft-deprecation messages on every re-export.
Velocity 0.0 · Last update 1h ago
Top 12 alternatives to feasts
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
ANALYTICS
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
ANALYTICS
comtradr's 1.0 line is a long tail of patches against a brittle UN trade API.
ANALYTICS
bbotk is generalizing from an optimizer toolkit into an evaluation framework.
ANALYTICS
patchwork stopped being a ggplot composer and became a page composer.
ANALYTICS
mlr3fselect turned feature selection into an asynchronous, distributable job
ANALYTICS
lime survives on compatibility patches years after its research moment
ANALYTICS
mlr3measures is systematically retrofitting sample weights across every metric
ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
ANALYTICS
mlr3filters grows one feature-selection filter at a time
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mlr3learners spends its releases absorbing upstream churn
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gutenbergr has been rebuilt around caching and mirror resilience
feasts 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 |
|---|---|---|---|---|
| feasts (baseline) | 0.0 | 0 | time-seriesr-statsdeprecation | feasts moves its plots to ggtime behind a 2-year deprecation |
| mlr3mbo | 2.5 | 0 | bayesian-optimizationmlr3hyperparameter-tuning | mlr3mbo 1.0.0 ships benchmark-derived default settings |
| loo | 2.5 | 0 | bayesiancross-validationstan | loo_compare returns a data.frame with new uncertainty columns |
| comtradr | 2.5 | 0 | trade dataapi wrapperun comtrade | — |
| bbotk | 2.5 | 0 | black-box optimizationmlr3async execution | EvalInstance base class separates evaluation from optimization |
| patchwork | 0.0 | 0 | ggplot2compositiontables | gt tables become first-class patchwork objects |
| mlr3fselect | 0.0 | 0 | feature-selectionmlr3machine-learning | Asynchronous feature selection arrives with FSelectorAsync |
| lime | 0.0 | 0 | interpretabilitymachine-learningr-stats | — |
| mlr3measures | 0.0 | 0 | metricsmlr3machine-learning | — |
| mlr3cluster | 0.0 | 0 | clusteringmlr3machine-learning | Nine new clustering learners in one release |
| mlr3filters | 0.0 | 0 | feature-selectionmlr3machine-learning | — |
| mlr3learners | 0.0 | 0 | mlr3machine-learningr-stats | — |
| gutenbergr | 0.0 | 0 | text-miningr-statscaching | — |
The 12 best feasts alternatives, in depth
1. mlr3mbo · velocity 2.5
Mlr3mbo picked its defaults from a benchmark study, not from taste.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “mlr3mbo 1.0.0 ships benchmark-derived default settings”.
Where feasts leans on time series, r stats and deprecation, mlr3mbo focuses on bayesian optimization, mlr3 and hyperparameter tuning.
mlr3mbo and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
2. loo · velocity 2.5
Loo keeps rewriting the diagnostics Bayesian modellers read off model comparison.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “loo_compare returns a data.frame with new uncertainty columns”.
Where feasts leans on time series, r stats and deprecation, loo focuses on bayesian, cross validation and stan.
loo and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. comtradr · velocity 2.5
Comtradr's 1.0 line is a long tail of patches against a brittle UN trade API.
Its velocity score of 2.5/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, comtradr focuses on trade data, api wrapper and un comtrade.
comtradr and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
4. bbotk · velocity 2.5
Bbotk is generalizing from an optimizer toolkit into an evaluation framework.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “EvalInstance base class separates evaluation from optimization”.
Where feasts leans on time series, r stats and deprecation, bbotk focuses on black box optimization, mlr3 and async execution.
bbotk and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
5. patchwork · velocity 0.0
Patchwork stopped being a ggplot composer and became a page composer.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “gt tables become first-class patchwork objects”.
Where feasts leans on time series, r stats and deprecation, patchwork focuses on ggplot2, composition and tables.
patchwork and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
6. mlr3fselect · velocity 0.0
Mlr3fselect turned feature selection into an asynchronous, distributable job.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Asynchronous feature selection arrives with FSelectorAsync”.
Where feasts leans on time series, r stats and deprecation, mlr3fselect focuses on feature selection, mlr3 and machine learning.
mlr3fselect and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3fselect trajectory → · Compare feasts vs mlr3fselect →
7. lime · velocity 0.0
Lime survives on compatibility patches years after its research moment.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, lime focuses on interpretability, machine learning and r stats.
lime and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
8. mlr3measures · velocity 0.0
Mlr3measures is systematically retrofitting sample weights across every metric.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, mlr3measures focuses on metrics, mlr3 and machine learning.
mlr3measures and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3measures trajectory → · Compare feasts vs mlr3measures →
9. mlr3cluster · velocity 0.0
Mlr3cluster went from a handful of clusterers to covering the field.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Nine new clustering learners in one release”.
Where feasts leans on time series, r stats and deprecation, mlr3cluster focuses on clustering, mlr3 and machine learning.
mlr3cluster and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3cluster trajectory → · Compare feasts vs mlr3cluster →
10. mlr3filters · velocity 0.0
Mlr3filters grows one feature-selection filter at a time.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, mlr3filters focuses on feature selection, mlr3 and machine learning.
mlr3filters and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3filters trajectory → · Compare feasts vs mlr3filters →
11. mlr3learners · velocity 0.0
Mlr3learners spends its releases absorbing upstream churn.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, mlr3learners focuses on mlr3, machine learning and r stats.
mlr3learners and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3learners trajectory → · Compare feasts vs mlr3learners →
12. gutenbergr · velocity 0.0
Gutenbergr has been rebuilt around caching and mirror resilience.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where feasts leans on time series, r stats and deprecation, gutenbergr focuses on text mining, r stats and caching.
gutenbergr and feasts have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full gutenbergr trajectory → · Compare feasts vs gutenbergr →
Frequently asked questions
What are the best alternatives to feasts?
The top feasts alternatives we currently track in analytics tools are mlr3mbo, loo, comtradr, bbotk, patchwork, ranked by recent ship velocity.
How is this list of feasts alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare feasts directly with one of these alternatives?
Yes — every card has a "Compare with feasts" link to a side-by-side /compare page.