mrbayes
mrbayes spent 2026 auditing its own Bayesian MR estimators for coding errors.
Comparison · Analytics
fastplyr vs modeltime.ensemble
A side-by-side editorial comparison of fastplyr and modeltime.ensemble — release velocity, themes, recent moves, and the top alternatives to consider.
fastplyr vs modeltime.ensemble: at a glance
| Feature | fastplyr | modeltime.ensemble |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 0.0 | 0.0 |
| Sparks · 30d | 0 | 0 |
| Top themes | dataframe-performance, dplyr-alternative, query-optimization, cran-policy | time series forecasting, ensembles, tidymodels, compatibility maintenance |
| Last editorial update | 49m ago | 2h ago |
| Website | Visit → | Visit → |
What is fastplyr?
A fast dplyr stand-in that keeps finding new places to skip work entirely.
fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.
What is modeltime.ensemble?
modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.
modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.
fastplyr vs modeltime.ensemble: editorial side-by-side
fastplyr
0.0ANALYTICS
A fast dplyr stand-in that keeps finding new places to skip work entirely.
◆ Current state
fastplyr reimplements the dplyr verbs on a faster backend, exposing f_summarise, f_mutate, f_reframe and a set of group metadata helpers alongside optimized joins and quantiles. The most recent release removes non-API C functions and raises the floor to R 4.5.0, a steep requirement that follows the C++17 requirement introduced a release earlier. The verb surface itself has been stable since 0.9.0.
◆ Where it's heading
The optimization strategy has shifted from making individual functions fast to reasoning about expressions before evaluating them — 0.9.9 began marking simple operators as group-unaware so expressions built only from them are evaluated across the whole data frame rather than per group. That is a structural bet: the package increasingly inspects what you wrote to decide how much work is actually needed. Running alongside it is a steady tightening of build requirements, with C++17, R 4.5.0 and CRAN's C API rules all landing within a year.
◆ Prediction
Expect the group-unaware classification to widen to more functions, since each addition compounds across every grouped expression, and expect the dependency floors to keep rising as the package tracks CRAN's compiled-code policy.
modeltime.ensemble
0.0ANALYTICS
modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.
◆ Current state
modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.
◆ Where it's heading
This is a package whose forecasting capability was settled by 2021 — recursive ensembles, per-series calibration — and whose recent life is dictated entirely by upstream tidymodels churn. New contributors did that compatibility work, including one from the tidymodels side. It now requires tune 2.0.0 and modeltime.resample 0.3.0, pinning it to the current tidymodels generation rather than straddling versions.
◆ Prediction
Expect the next release to follow the next tune or modeltime.resample breaking change rather than to introduce new ensembling methods.
Alternatives to fastplyr and modeltime.ensemble
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either fastplyr or modeltime.ensemble.
lineup2
lineup2 ships once every few years, and 2026's release is a logo and a core-count tweak.
stringi
stringi has spent two years on build hardening since its Unicode 15.1 reset.
OHPL
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
trias
Belgium's invasive-species indicator toolkit is in steady refinement, one plotting edge case at a time.
icesSAG
The ICES stock assessment client took upload away in 2024 and spent two years giving it back.
See all fastplyr alternatives → · See all modeltime.ensemble alternatives →
Recent activity from fastplyr and modeltime.ensemble
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 4mo agofastplyrNon-API C functions dropped, R 4.5.0 now required
- 8mo agofastplyrIn-place sorting arrives with a C++17 requirement
- 10mo agofastplyrGroup-unaware expressions evaluated on the whole frame
- 11mo agomodeltime.ensembleRealigned for tune 2.0.0 resampling changes
- 11mo agomodeltime.ensembleDrops the tidyverse dependency ahead of tune 2.0
- 1y agofastplyrf_mutate and f_reframe complete the verb set
- 1y agofastplyrDynamic argument evaluation and f_pull
- 1y agofastplyrf_fill added and grouped joins repaired
- 5y agomodeltime.ensemblePer-series calibration IDs and parallel refitting
- 5y agomodeltime.ensembleRecursive ensembles for single and panel series ⚡
Frequently asked questions
What is the difference between fastplyr and modeltime.ensemble?
They serve adjacent needs but don't currently overlap on shipped themes. fastplyr and modeltime.ensemble are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is fastplyr better than modeltime.ensemble?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastplyr and modeltime.ensemble are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to fastplyr?
Top fastplyr alternatives in Analytics are ranked by recent ship velocity. Browse the "fastplyr alternatives" section above for the current picks, or visit /alternatives/fastplyr for the full list with editorial commentary on each.
What are the best alternatives to modeltime.ensemble?
Top modeltime.ensemble alternatives in Analytics are ranked by recent ship velocity. Browse the "modeltime.ensemble alternatives" section above for the current picks, or visit /alternatives/modeltime-ensemble for the full list with editorial commentary on each.