ANALYTICS
mlr3mbo picked its defaults from a benchmark study, not from taste
Alternatives · analytics tools
loo alternatives
The best loo alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 13, 2026
Looking for the best alternatives to loo? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, loo shipped 0 meaningful updates in the last 30 days and carries a velocity score of 2.5 out of 10 in 2026. The alternatives below are ranked the same way, so you're comparing real release momentum, not marketing claims.
About loo
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
loo computes leave-one-out cross-validation and model comparison for Bayesian models in the Stan ecosystem. Two releases in this window changed what users actually read: 2.7.0 replaced the fixed Pareto-k thresholds with sample-size-dependent ones and dropped the middle category, and 2.10.0 reshaped loo_compare's output into a data.frame with new uncertainty columns. The releases between are diagnostic robustness fixes and moment-matching corrections.
Velocity 2.5 · Last update 57m ago
Top 12 alternatives to loo
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
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
ANALYTICS
mlr3learners spends its releases absorbing upstream churn
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gutenbergr has been rebuilt around caching and mirror resilience
ANALYTICS
ggspatial finishes its move off raster and onto terra
loo 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 |
|---|---|---|---|---|
| loo (baseline) | 2.5 | 0 | bayesiancross-validationstan | loo_compare returns a data.frame with new uncertainty columns |
| mlr3mbo | 2.5 | 0 | bayesian-optimizationmlr3hyperparameter-tuning | mlr3mbo 1.0.0 ships benchmark-derived default settings |
| 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 | — |
| ggspatial | 0.0 | 0 | geospatialggplot2r-stats | — |
The 12 best loo 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 loo leans on bayesian, cross validation and stan, mlr3mbo focuses on bayesian optimization, mlr3 and hyperparameter tuning.
mlr3mbo and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
2. 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 loo leans on bayesian, cross validation and stan, comtradr focuses on trade data, api wrapper and un comtrade.
comtradr and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. 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 loo leans on bayesian, cross validation and stan, bbotk focuses on black box optimization, mlr3 and async execution.
bbotk and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
4. 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 loo leans on bayesian, cross validation and stan, patchwork focuses on ggplot2, composition and tables.
patchwork and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
5. 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 loo leans on bayesian, cross validation and stan, mlr3fselect focuses on feature selection, mlr3 and machine learning.
mlr3fselect and loo 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 loo vs mlr3fselect →
6. 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 loo leans on bayesian, cross validation and stan, lime focuses on interpretability, machine learning and r stats.
lime and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
7. 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 loo leans on bayesian, cross validation and stan, mlr3measures focuses on metrics, mlr3 and machine learning.
mlr3measures and loo 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 loo vs mlr3measures →
8. 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 loo leans on bayesian, cross validation and stan, mlr3cluster focuses on clustering, mlr3 and machine learning.
mlr3cluster and loo 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 loo vs mlr3cluster →
9. 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 loo leans on bayesian, cross validation and stan, mlr3filters focuses on feature selection, mlr3 and machine learning.
mlr3filters and loo 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 loo vs mlr3filters →
10. mlr3learners · velocity 0.0
Mlr3learners spends its releases absorbing upstream churn.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where loo leans on bayesian, cross validation and stan, mlr3learners focuses on mlr3, machine learning and r stats.
mlr3learners and loo 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 loo vs mlr3learners →
11. 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 loo leans on bayesian, cross validation and stan, gutenbergr focuses on text mining, r stats and caching.
gutenbergr and loo have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
12. ggspatial · velocity 0.0
Ggspatial finishes its move off raster and onto terra.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where loo leans on bayesian, cross validation and stan, ggspatial focuses on geospatial, ggplot2 and r stats.
ggspatial and loo 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 loo?
The top loo alternatives we currently track in analytics tools are mlr3mbo, comtradr, bbotk, patchwork, mlr3fselect, ranked by recent ship velocity.
How is this list of loo alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare loo directly with one of these alternatives?
Yes — every card has a "Compare with loo" link to a side-by-side /compare page.