tidyr
tidyr replaced separate() with a family that says what it does.
A side-by-side editorial comparison of cmdstanpy and performance — release velocity, themes, recent moves, and the top alternatives to consider.
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
CmdStanPy is at v1.3.0, which added a diagnose method on CmdStanModel, sampler timing information, create_inits() across the stanfit classes, and much faster MCMC CSV parsing. Every release in this window opens with the same notice: the next non-bugfix release will be 2.0 and will remove existing deprecations. In line with that, 1.3.0 drops Python 3.8 and renames the metric argument to inv_metric.
performance keeps adding ways to check a model you have already fitted.
performance is at 0.17.1, which added check_priors() for prior predictive checks on Bayesian models and gave check_overdispersion(), check_model() and check_predictions() arguments to control residual type and plot range. The releases before it are a similar mix: a -2LL criterion column in test_likelihoodratio(), Bayesian predictive checks routed through modelbased, and in 0.16.0 a set of breaking renames including RMSA to the correct RMSR.
CmdStanPy is at v1.3.0, which added a diagnose method on CmdStanModel, sampler timing information, create_inits() across the stanfit classes, and much faster MCMC CSV parsing. Every release in this window opens with the same notice: the next non-bugfix release will be 2.0 and will remove existing deprecations. In line with that, 1.3.0 drops Python 3.8 and renames the metric argument to inv_metric.
The package tracks upstream Stan and prepares for its own break. New inference methods arrive as Stan ships them — Laplace with Stan 2.32, Pathfinder with 2.33 — while the interface work is mostly deprecation staging and CSV input-output performance. The repeated 2.0 warning across two years of releases suggests the cut has been deferred more than once.
The notice at the top of every release points to 2.0 as the next non-bugfix version, removing the deprecations staged here including the metric argument.
performance is at 0.17.1, which added check_priors() for prior predictive checks on Bayesian models and gave check_overdispersion(), check_model() and check_predictions() arguments to control residual type and plot range. The releases before it are a similar mix: a -2LL criterion column in test_likelihoodratio(), Bayesian predictive checks routed through modelbased, and in 0.16.0 a set of breaking renames including RMSA to the correct RMSR.
Two consistent habits. Diagnostics keep gaining arguments to narrow what is examined — ppc_range, x_limits, maximum_dots, show_ci — which reads as a package being used on models large and awkward enough that the defaults stopped working. And simulated residuals via DHARMa keep displacing standard ones as the basis for the checks themselves.
With check_priors() newly added and Bayesian predictive checks now routed through modelbased, the next release most likely extends the Bayesian diagnostic set rather than reworking the frequentist checks.
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 cmdstanpy or performance.
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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.
See all cmdstanpy alternatives → · See all performance alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — bayesian — within Analytics. cmdstanpy and performance 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. cmdstanpy and performance 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 cmdstanpy alternatives in Analytics are ranked by recent ship velocity. Browse the "cmdstanpy alternatives" section above for the current picks, or visit /alternatives/cmdstanpy for the full list with editorial commentary on each.
Top performance alternatives in Analytics are ranked by recent ship velocity. Browse the "performance alternatives" section above for the current picks, or visit /alternatives/easystats-performance for the full list with editorial commentary on each.