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fastglm vs impIndicator

A side-by-side editorial comparison of fastglm and impIndicator — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

fastglm vs impIndicator: at a glance

FeaturefastglmimpIndicator
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesstatistical-computing, generalized-linear-models, cpp, r-packagebiodiversity, invasive-species, occurrence-cubes, uncertainty
Last editorial update3h ago40m ago
WebsiteVisit →Visit →

What is fastglm?

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.

Read the full fastglm trajectory →

What is impIndicator?

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.

Read the full impIndicator trajectory →

fastglm vs impIndicator: editorial side-by-side

F
fastglm
ANALYTICS
0.0

A fast GLM solver stops being one function and becomes a count-model family

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

I
impIndicator
ANALYTICS
0.0

Biodiversity impact indicators settle their vocabulary before 1.0

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

Alternatives to fastglm and impIndicator

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.

See all fastglm alternatives → · See all impIndicator alternatives →

Recent activity from fastglm and impIndicator

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 2mo agofastglmFirth generalised to all families, plus SQUAREM and stability fixes
  2. 3mo agofastglmCRAN release 0.1.0
  3. 4mo agoimpIndicatorIndicator functions renamed; scores no longer site-normalised
  4. 5mo agoimpIndicatorUncertainty estimation for impact indicators via dubicube
  5. 7mo agoimpIndicatorExport impact_cube_data() for building impact occurrence cubes
  6. 8mo agoimpIndicatorIndicators can be computed for a user-supplied region
  7. 8mo agoimpIndicatorimpIndicator 0.3.2
  8. 9mo agoimpIndicatorimpIndicator 0.3.1
  9. 4y agofastglmC++ headers exposed for linking
  10. 7y agofastglmFirst CRAN release of the C++ IRLS solver

Frequently asked questions

What is the difference between fastglm and impIndicator?

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.

Is fastglm better than impIndicator?

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.

What are the best alternatives to fastglm?

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.

What are the best alternatives to impIndicator?

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.