dowhy
DoWhy adds one estimation method a year and keeps its identification edge.
A side-by-side editorial comparison of Distributions.jl and ggplot2 — release velocity, themes, recent moves, and the top alternatives to consider.
Julia's distribution library grinds forward one distribution at a time
Distributions.jl ships small, frequent releases against a large and settled API surface. Recent work splits between correctness fixes to individual distributions (LogitNormal formulas, Semicircle quantiles, Truncated Chernoff), incremental fitting support such as sufficient statistics and MLE for Chi and Chisq, and infrastructure moves like more consistent error types and global sparsity tracing through constructors.
ggplot2 swapped its object system out from under a decade of downstream code
The 4.0.0 release replaced ggplot2's S3 internals with S7 and made every geom's defaults settable from the theme, both breaking changes. The three releases since have been hotfixes cleaning up the fallout - regressions in geom_area(), position_stack() and the scale and guide systems - plus rlang interoperability repairs. The one genuinely new feature in that window is a quantile.type argument on boxplots.
Distributions.jl ships small, frequent releases against a large and settled API surface. Recent work splits between correctness fixes to individual distributions (LogitNormal formulas, Semicircle quantiles, Truncated Chernoff), incremental fitting support such as sufficient statistics and MLE for Chi and Chisq, and infrastructure moves like more consistent error types and global sparsity tracing through constructors.
The arc is consolidation rather than expansion: dependencies are being pruned and internals made more predictable so the package composes cleanly with the rest of the Julia numerical stack. Support for sparsity tracing and looser MvNormal type aliases both point at making the library easier to drive from automatic-differentiation and optimization code.
Expect the same cadence of per-distribution fixes and fitting-method additions, with continued work on making constructors transparent to tracing and AD tooling. Nothing in these entries signals a major version or API break.
The 4.0.0 release replaced ggplot2's S3 internals with S7 and made every geom's defaults settable from the theme, both breaking changes. The three releases since have been hotfixes cleaning up the fallout - regressions in geom_area(), position_stack() and the scale and guide systems - plus rlang interoperability repairs. The one genuinely new feature in that window is a quantile.type argument on boxplots.
This is the tail of a long-telegraphed migration: 3.5.2 existed largely to give downstream packages the is_*() predicates and accessor functions they would need before 4.0 landed. With theme(geom) and from_theme(), styling is consolidating into the theme rather than being repeated per layer, which is the direction the extension ecosystem now has to follow.
Expect further 4.0.x patches as S7-related regressions surface in extension packages, and more of the per-geom default surface to migrate into element_geom(). The entries give no indication of a 4.1 feature line yet.
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 Distributions.jl or ggplot2.
DoWhy adds one estimation method a year and keeps its identification edge.
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.
StatsBase.jl is in caretaker mode — correctness fixes in, dependency bumps out.
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
See all Distributions.jl alternatives → · See all ggplot2 alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Distributions.jl is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. Distributions.jl is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Distributions.jl alternatives in Analytics are ranked by recent ship velocity. Browse the "Distributions.jl alternatives" section above for the current picks, or visit /alternatives/distributions-jl for the full list with editorial commentary on each.
Top ggplot2 alternatives in Analytics are ranked by recent ship velocity. Browse the "ggplot2 alternatives" section above for the current picks, or visit /alternatives/ggplot2 for the full list with editorial commentary on each.