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Comparison · Analytics
shiny vs statsmodels
A side-by-side editorial comparison of shiny and statsmodels — release velocity, themes, recent moves, and the top alternatives to consider.
shiny vs statsmodels: at a glance
What is shiny?
Shiny made reactive apps observable, then gave them a way to tear themselves down
Version 1.12.0 added OpenTelemetry support through the {otel} package, emitting spans for session start and end, reactive updates and individual reactive expressions, with collection depth set by an option or environment variable. The releases since have refined it - scoped collection controls in 1.12.1, cleaner stack traces in 1.13.0 - while 1.14.0 turned to lifecycle, adding session$destroy() on module proxies and a non-blocking startApp() for driving apps programmatically.
What is statsmodels?
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.
shiny vs statsmodels: editorial side-by-side
shiny
0.0ANALYTICS
Shiny made reactive apps observable, then gave them a way to tear themselves down
◆ Current state
Version 1.12.0 added OpenTelemetry support through the {otel} package, emitting spans for session start and end, reactive updates and individual reactive expressions, with collection depth set by an option or environment variable. The releases since have refined it - scoped collection controls in 1.12.1, cleaner stack traces in 1.13.0 - while 1.14.0 turned to lifecycle, adding session$destroy() on module proxies and a non-blocking startApp() for driving apps programmatically.
◆ Where it's heading
The framework is addressing the two things that make Shiny apps hard to run in production: you could not see inside the reactive graph, and you could not reliably dispose of parts of it. Tracing answers the first; scoped destruction of module session proxies answers the second. Both are aimed at long-lived, dynamically composed apps rather than at the single-file demo.
◆ Prediction
Expect the OpenTelemetry attribute names to settle once the deprecated spellings are dropped, and more of the reactive lifecycle to gain explicit teardown hooks now that session$destroy() has established the pattern. Editor integration is a likely area for follow-up after the Ark breakpoint support.
statsmodels
0.0ANALYTICS
statsmodels ships only what the ecosystem breaks — six releases, no new statistics.
◆ Current state
Every release in this window is a compatibility release. 0.14.2 and 0.14.3 absorbed NumPy 2, 0.14.5 fixed an import failure caused by SciPy 1.16, and 0.14.6 did the same for pandas 3.0. The only additive change across two years is Pyodide support in 0.14.4, described in its own notes as one feature and no fixes. A 0.15.0.dev0 tag exists from 2023 and has not been followed by a 0.15 release.
◆ Where it's heading
The library is being kept alive rather than developed: each release answers a break introduced upstream, and the interval between them is set by the NumPy, SciPy and pandas release calendars rather than by anything statsmodels is building. Two consecutive releases whose stated purpose was restoring the ability to import the package is the sharpest available signal about maintainer bandwidth. The 0.15 line remains a dev tag with no visible progress toward a release.
◆ Prediction
The next release is most likely another compatibility patch triggered by a NumPy, SciPy or pandas major, and nothing in these entries indicates 0.15 is close.
Alternatives to shiny and statsmodels
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 shiny or statsmodels.
OpenHouse
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
StatsBase.jl
StatsBase.jl is in caretaker mode — correctness fixes in, dependency bumps out.
iris
Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.
ggplot2
ggplot2 swapped its object system out from under a decade of downstream code
See all shiny alternatives → · See all statsmodels alternatives →
Recent activity from shiny and statsmodels
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoshinyModule scopes gain session$destroy() for reactive teardown
- 5mo agoshinyInteractive breakpoints under Ark; cleaner render stack traces
- 8mo agoshinyScoped OpenTelemetry collection controls
- 8mo agostatsmodels0.14.6: restores importing under pandas 3.0
- 8mo agoshinyShiny adds OpenTelemetry tracing of the reactive graph ⚡
- 1y agoshinyRegression fixes for input bindings and label updates
- 1y agostatsmodels0.14.5: restores importing under SciPy 1.16
- 1y agoshinyAuto-reload covers modules and support files
- 1y agostatsmodels0.14.4: Pyodide support
- 1y agostatsmodels0.14.3: NumPy 2 environments and corrected macOS builds
- 2y agostatsmodels0.14.2: full NumPy 2 compatibility
- 2y agostatsmodelsRelease 0.14.1
Frequently asked questions
What is the difference between shiny and statsmodels?
They serve adjacent needs but don't currently overlap on shipped themes. shiny and statsmodels 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.
Is shiny better than statsmodels?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. shiny and statsmodels 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.
What are the best alternatives to shiny?
Top shiny alternatives in Analytics are ranked by recent ship velocity. Browse the "shiny alternatives" section above for the current picks, or visit /alternatives/r-shiny for the full list with editorial commentary on each.
What are the best alternatives to statsmodels?
Top statsmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "statsmodels alternatives" section above for the current picks, or visit /alternatives/statsmodels for the full list with editorial commentary on each.