fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of fastglm and hoopr — release velocity, themes, recent moves, and the top alternatives to consider.
A fast GLM solver stops being one function and becomes a count-model family
fastglm ran C++ IRLS for standard generalized linear models for six years with almost no releases. In May 2026 it added three top-level model types — negative binomial with jointly estimated dispersion, hurdle, and zero-inflated — each with the entire fitting driver in C++ rather than an R loop around a C++ kernel. The following release generalised Firth bias reduction to every standard family across dense, sparse and streaming backends.
hoopR rebuilds its HTTP layer on httr2 to stop segfaulting on modern systems
hoopR is the sportsdataverse R package for basketball data, wrapping ESPN, NBA Stats, NBA G-League, NCAA and KenPom behind a single set of loaders. Version 3.0.0 replaces httr with httr2 across every one of those backends, drops httr from Imports, and routes all calls through shared internal retry and response helpers. The change is breaking, and it exists because the old stack segfaulted against libcurl 8.x and curl 7.0.0.
fastglm ran C++ IRLS for standard generalized linear models for six years with almost no releases. In May 2026 it added three top-level model types — negative binomial with jointly estimated dispersion, hurdle, and zero-inflated — each with the entire fitting driver in C++ rather than an R loop around a C++ kernel. The following release generalised Firth bias reduction to every standard family across dense, sparse and streaming backends.
The package changed what it is. Through 0.0.3 it was a drop-in replacement for glm() competing on speed; from 0.1.0 it targets the models people leave base R for — MASS::glm.nb, pscl::hurdle, pscl::zeroinfl — and reimplements their full estimation loops natively. The 0.1.1 follow-up is consolidation on that new surface: Firth generalised past binomial logit, SQUAREM acceleration on the zero-inflation EM driver, and a run of clamping guards and initialization fixes on the families most prone to overflow.
The numerical-stability work in 0.1.1 clusters on Tweedie and the inverse and sqrt link families, which suggests those paths are the newest and least exercised — expect further correctness fixes there before new model types.
hoopR is the sportsdataverse R package for basketball data, wrapping ESPN, NBA Stats, NBA G-League, NCAA and KenPom behind a single set of loaders. Version 3.0.0 replaces httr with httr2 across every one of those backends, drops httr from Imports, and routes all calls through shared internal retry and response helpers. The change is breaking, and it exists because the old stack segfaulted against libcurl 8.x and curl 7.0.0.
The package's history is two distinct eras. Through 2021-2023 it grew by endpoint accretion — ESPN stat functions, G-League coverage, the NBA live and boxscore V3 families, on-court players in play-by-play — expanding what could be pulled. The recent work is consolidation instead: one HTTP pipeline, one messaging library, data served from the shared sportsdataverse-data releases rather than per-package repositories. The centre of gravity has moved from adding endpoints to making the plumbing survive its dependencies.
With the HTTP layer unified behind shared helpers, expect the sibling sportsdataverse packages to follow the same httr2 migration, and hoopR's own next releases to resume endpoint work now that requests run through one pipeline.
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 fastglm or hoopr.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all fastglm alternatives → · See all hoopr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. fastglm and hoopr 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. fastglm and hoopr 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 fastglm alternatives in Analytics are ranked by recent ship velocity. Browse the "fastglm alternatives" section above for the current picks, or visit /alternatives/fastglm for the full list with editorial commentary on each.
Top hoopr alternatives in Analytics are ranked by recent ship velocity. Browse the "hoopr alternatives" section above for the current picks, or visit /alternatives/hoopr for the full list with editorial commentary on each.