fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of fastglm and impIndicator — 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.
Biodiversity impact indicators settle their vocabulary before 1.0
impIndicator computes indicators of alien-species impact from GBIF-style occurrence cubes, producing species-level, site-level and regional measures with visualisation. The latest release renames the three headline functions to compute_species_indicator(), compute_site_indicator() and compute_regional_indicator(), drops the division by total occupied sites, and fixes the exponential transformation of impact categories into scores. It is part of the b-cubed-eu family and leans on sibling tooling rather than reimplementing it.
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.
impIndicator computes indicators of alien-species impact from GBIF-style occurrence cubes, producing species-level, site-level and regional measures with visualisation. The latest release renames the three headline functions to compute_species_indicator(), compute_site_indicator() and compute_regional_indicator(), drops the division by total occupied sites, and fixes the exponential transformation of impact categories into scores. It is part of the b-cubed-eu family and leans on sibling tooling rather than reimplementing it.
Two threads run through the recent releases. One is uncertainty: 0.6.0 wires in dubicube for cross-validation and uncertainty estimation on the indicators, moving output from point estimates toward quantified confidence. The other is scoping and naming — user-supplied sf regions in 0.4.0, occurrence-cube construction in 0.5.0, then the 0.6.1 rename — the pattern of a package tightening its public vocabulary as it approaches a stable release.
With the naming settled and uncertainty estimation in place, the next step is most likely consolidation toward a 1.0 — documentation and vignettes against the renamed functions rather than further indicator types.
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 impIndicator.
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 impIndicator alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fastglm and impIndicator 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 impIndicator 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 impIndicator alternatives in Analytics are ranked by recent ship velocity. Browse the "impIndicator alternatives" section above for the current picks, or visit /alternatives/impindicator for the full list with editorial commentary on each.