modeltime
modeltime built conformal intervals in, then went quiet on features.
A side-by-side editorial comparison of dowhy and tidyr — release velocity, themes, recent moves, and the top alternatives to consider.
DoWhy adds one estimation method a year and keeps its identification edge.
DoWhy is at v0.14, which added a doubly robust estimator and Python 3.13 support. The releases before it followed the same shape: v0.13 brought the Generalized Adjustment Criterion for identification, v0.12 added time-series effect estimation and a rank-based anomaly scorer. Between the feature releases sit patch versions handling pandas and CUDA breakage.
tidyr replaced separate() with a family that says what it does.
tidyr is at 1.3.2, a collection of argument additions — fill() gains .by, expand_grid() gains .vary — and better error messages around unchop() and pivot_wider_spec(). The structural work is 1.3.0, which introduced separate_wider_delim(), separate_wider_position(), separate_wider_regex(), separate_longer_delim() and separate_longer_position() as thorough replacements for separate(), extract() and separate_rows().
DoWhy is at v0.14, which added a doubly robust estimator and Python 3.13 support. The releases before it followed the same shape: v0.13 brought the Generalized Adjustment Criterion for identification, v0.12 added time-series effect estimation and a rank-based anomaly scorer. Between the feature releases sit patch versions handling pandas and CUDA breakage.
Two threads run through the window. The identification side — DoWhy's differentiator against libraries that only estimate — keeps gaining criteria, from frontdoor with multiple variables through the Generalized Adjustment Criterion. The GCM side grows separately with missing-data handling, classifier selection logic and calibration work. The two halves are converging on a single API rather than staying separate entry points.
Given the pace of one estimator or criterion per release and the experimental flags still on missing-data support in GCM, the next release most likely promotes existing experimental features rather than opening a new estimation family.
tidyr is at 1.3.2, a collection of argument additions — fill() gains .by, expand_grid() gains .vary — and better error messages around unchop() and pivot_wider_spec(). The structural work is 1.3.0, which introduced separate_wider_delim(), separate_wider_position(), separate_wider_regex(), separate_longer_delim() and separate_longer_position() as thorough replacements for separate(), extract() and separate_rows().
Two habits define this window. Verbs are being split into explicitly named variants rather than overloaded with arguments, which is what the separate_* family does to separate(). And .by is spreading as the standard way to express grouping inline — nest(.by=) in 1.3.0, fill(.by=) in 1.3.2 — pulling users away from wrapping calls in group_by().
Given that .by has now reached fill() and nest(), the next release most likely extends the same argument to further verbs rather than reworking another function family.
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 dowhy or tidyr.
modeltime built conformal intervals in, then went quiet on features.
performance keeps adding ways to check a model you have already fitted.
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. dowhy and tidyr 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. dowhy and tidyr 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 dowhy alternatives in Analytics are ranked by recent ship velocity. Browse the "dowhy alternatives" section above for the current picks, or visit /alternatives/dowhy for the full list with editorial commentary on each.
Top tidyr alternatives in Analytics are ranked by recent ship velocity. Browse the "tidyr alternatives" section above for the current picks, or visit /alternatives/tidyr for the full list with editorial commentary on each.