RMVMR
RMVMR is being tidied in lockstep with MVMR, the package it wraps
A side-by-side editorial comparison of filtro and modeltime.ensemble — release velocity, themes, recent moves, and the top alternatives to consider.
filtro moves to S7 and multiplies its feature-scoring methods in a single release.
filtro supplies feature-selection filter scores for the tidymodels stack. Version 0.2.0 adds five scoring methods — correlation, random forest importance, information gain, ROC AUC and cross tabulation — and moves the package from S3 to S7. It also gains a ranking layer: show_best_score_* and rank_best_score_* helpers for a single score, plus desirability-function helpers for optimising across several scores at once.
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.
filtro supplies feature-selection filter scores for the tidymodels stack. Version 0.2.0 adds five scoring methods — correlation, random forest importance, information gain, ROC AUC and cross tabulation — and moves the package from S3 to S7. It also gains a ranking layer: show_best_score_* and rank_best_score_* helpers for a single score, plus desirability-function helpers for optimising across several scores at once.
The package is being built out on two axes at once — the catalogue of scores, and the machinery for choosing between them. The desirability functions are the more telling half, since they assume users will filter on several criteria rather than one. Adopting S7 while still pre-1.0 suggests the object model is being settled before the API is frozen.
Expect more scoring methods on the same S7 interface and a 1.0 release once the score and ranking APIs stop moving; the ranking helpers' naming is the most likely thing to change first.
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.
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.
Expect the next release to follow the next tune or modeltime.resample breaking change rather than to introduce new ensembling methods.
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 filtro or modeltime.ensemble.
RMVMR is being tidied in lockstep with MVMR, the package it wraps
geoarrow tracks the GeoArrow spec and otherwise just keeps compiling
n2khab keeps retracting interpretations of habitat data it can't actually support
tidypolars is grinding toward complete dplyr coverage, one supported function at a time
OneSampleMR found that argument order in a formula was silently changing its estimates
bpbounds found the same swapped-cell bug twice and clamped its bounds back into range
See all filtro alternatives → · See all modeltime.ensemble alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — tidymodels, r package — within Analytics. filtro 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. filtro 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.
Top filtro alternatives in Analytics are ranked by recent ship velocity. Browse the "filtro alternatives" section above for the current picks, or visit /alternatives/filtro-r for the full list with editorial commentary on each.
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.