dowhy
DoWhy adds one estimation method a year and keeps its identification edge.
A side-by-side editorial comparison of Distributions.jl and iris — 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.
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
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.
Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.
The version numbers say a mature Met Office library is being maintained on a predictable schedule; nothing in the published entries says what is being maintained. Until the project puts release content in the tag body, its public trail will read as cadence without substance, and readers have to leave the feed to learn anything. The pattern has been identical across four consecutive releases, so it is a deliberate publishing choice rather than an oversight.
Expect v3.17.0rc0 around late 2026 on the same schedule, carrying the same boilerplate — the notes will again live on the documentation site rather than in the release entry.
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 iris.
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.
Shiny made reactive apps observable, then gave them a way to tear themselves down
See all Distributions.jl alternatives → · See all iris 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 and iris are shipping at a similar cadence (velocity 2.5 vs 2.5, 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. Distributions.jl and iris are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). 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 iris alternatives in Analytics are ranked by recent ship velocity. Browse the "iris alternatives" section above for the current picks, or visit /alternatives/scitools-iris for the full list with editorial commentary on each.