silx
silx settles into maintenance a release after its PySide6 migration
A side-by-side editorial comparison of effectplots and Plotly — release velocity, themes, recent moves, and the top alternatives to consider.
A young ALE and PDP plotting package that rebuilt its numeric core after a data-corrupting bug.
effectplots computes and plots partial dependence, ALE, and observed-versus-predicted effect curves for fitted models. It reached CRAN in November 2024 and shipped three releases in the four months after. The 0.2.0 release is the pivot: an outlier-clipping routine that silently modified the caller's data frame was fixed, the numeric path was rewritten for speed and memory, and the plotting and category-collapsing defaults were reset.
Plotly is turning its cloud into a metered compute platform with an enterprise on-ramp.
Plotly ships on two tracks. Plotly Studio, the desktop AI app-builder, releases every one to two weeks and has spent v0.0.80 through v0.0.86 on credential handling, reasoning transparency, personalization and now the reliability of the agent session engine itself. Plotly Cloud is the louder track: since late May it has added viewer-seat pricing, domain verification, per-app compute modes with credit-metered billing, and customer-owned domains with managed TLS.
effectplots computes and plots partial dependence, ALE, and observed-versus-predicted effect curves for fitted models. It reached CRAN in November 2024 and shipped three releases in the four months after. The 0.2.0 release is the pivot: an outlier-clipping routine that silently modified the caller's data frame was fixed, the numeric path was rewritten for speed and memory, and the plotting and category-collapsing defaults were reset.
After 0.2.0 the work turns to the awkward cases - missing values on the x axis, explicit and empty factor levels, discrete grid detection. The package is also widening past a single modelling ecosystem: h2o support and tidymodels examples arrived with 0.2.0, and fcut() was exported as a fast replacement for cut(). Release notes are issue-numbered throughout, so the roadmap is effectively the issue tracker.
Expect continued default tuning around collapse_m and discrete_m plus more model-backend coverage; the cadence points to another batch of issue fixes rather than a new plot type.
Plotly ships on two tracks. Plotly Studio, the desktop AI app-builder, releases every one to two weeks and has spent v0.0.80 through v0.0.86 on credential handling, reasoning transparency, personalization and now the reliability of the agent session engine itself. Plotly Cloud is the louder track: since late May it has added viewer-seat pricing, domain verification, per-app compute modes with credit-metered billing, and customer-owned domains with managed TLS.
The Cloud releases are assembling the standard pieces of a hosting business in order — identity first (domain verification, explicitly framed as the step before SSO), then billing (viewer seats, then metered compute credits), and now production-grade serving (custom domains, automatic certificate renewal). Studio is being hardened as the authoring front end that feeds it: Universal Deployment pushed beyond Dash apps, credentials saved once and reused, a Winget channel to widen Windows installs, and in v0.0.86 a rebuilt session engine plus automatic retries so agent runs survive expired tokens. The two tracks converge on one funnel — author in Studio, deploy to Cloud, pay by compute consumed.
The Domain Verification entry names SSO as the next step and places it in the Enterprise tier, so single sign-on is the most likely Cloud release next. Studio should hold its one-to-two-week cadence, with the newly added app thumbnails pointing toward more work on browsing and organizing generated apps.
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 effectplots or Plotly.
silx settles into maintenance a release after its PySide6 migration
aniread stops asking you to know which tracker wrote the file
Rho's release machinery finally produced a stable build — and it shipped no new product.
Usermaven closed the loop: data comes in from anywhere, and now it goes back out.
OpenCTI spends a release unblocking queues and hardening upserts
Mimir's feed is a weekly Helm bot, with the 3.2 candidate the only real release in months
See all effectplots alternatives → · See all Plotly alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Plotly is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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. Plotly is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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 effectplots alternatives in Analytics are ranked by recent ship velocity. Browse the "effectplots alternatives" section above for the current picks, or visit /alternatives/effectplots for the full list with editorial commentary on each.
Top Plotly alternatives in Analytics are ranked by recent ship velocity. Browse the "Plotly alternatives" section above for the current picks, or visit /alternatives/plotly for the full list with editorial commentary on each.