tidyr
tidyr replaced separate() with a family that says what it does.
A side-by-side editorial comparison of performance and PySCF — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Quantum chemistry package adding whole method families every release.
PySCF is a Python-based quantum chemistry toolkit, and its release cadence is unusually feature-dense: each version lands multiple new electronic-structure methods rather than polishing existing ones. Recent releases have built out the GW/BSE excited-state stack, k-point RPA for periodic systems, higher-order coupled cluster, and multi-state PDFT with analytical gradients. Platform work runs alongside — Windows DLL compatibility, GPU4PySCF interfacing, and configurable einsum backends.
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.
PySCF is a Python-based quantum chemistry toolkit, and its release cadence is unusually feature-dense: each version lands multiple new electronic-structure methods rather than polishing existing ones. Recent releases have built out the GW/BSE excited-state stack, k-point RPA for periodic systems, higher-order coupled cluster, and multi-state PDFT with analytical gradients. Platform work runs alongside — Windows DLL compatibility, GPU4PySCF interfacing, and configurable einsum backends.
Two arcs are visible. The methods arc is pushing toward periodic (PBC) parity with molecular calculations — PBC GW, PBC RPA, SOC-ECP for PBC DFT, QM/MM for periodic systems all landed in this window. The infrastructure arc is about getting PySCF to run where it previously did not: Windows, GPUs, alternative tensor-contraction backends.
The PBC catch-up should continue, since each release closes another gap between molecular and periodic implementations of the same method; the entries do not indicate which gap is next.
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 performance or PySCF.
tidyr replaced separate() with a family that says what it does.
modeltime built conformal intervals in, then went quiet on features.
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
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.
See all performance alternatives → · See all PySCF alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. PySCF 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. PySCF 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 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.
Top PySCF alternatives in Analytics are ranked by recent ship velocity. Browse the "PySCF alternatives" section above for the current picks, or visit /alternatives/pyscf for the full list with editorial commentary on each.