simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
Comparison · Analytics
fastglm vs ibis.iSDM
A side-by-side editorial comparison of fastglm and ibis.iSDM — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
fastglm vs ibis.iSDM: at a glance
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.
What is ibis.iSDM?
A raster-to-terra migration is the only readable change in a feed of merge notes.
ibis.iSDM fits integrated species distribution models in R. Its release notes are GitHub's auto-generated pull-request lists, so most tags say only which branch was merged and by whom. The one release with a written note, 0.0.5, records the migration from raster to terra across the whole package, with an explicit warning that established code may break.
fastglm vs ibis.iSDM: editorial side-by-side
fastglm
0.0ANALYTICS
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.
ibis.iSDM
0.0ANALYTICS
A raster-to-terra migration is the only readable change in a feed of merge notes.
◆ Current state
ibis.iSDM fits integrated species distribution models in R. Its release notes are GitHub's auto-generated pull-request lists, so most tags say only which branch was merged and by whom. The one release with a written note, 0.0.5, records the migration from raster to terra across the whole package, with an explicit warning that established code may break.
◆ Where it's heading
Direction cannot be read from this feed with any confidence - three of the four visible tags carry nothing beyond merge titles and a full-changelog link. What is visible is a 2023 spent on dependency modernisation and dev-branch merges, ending with a 0.1.1 tag that December and nothing since.
◆ Prediction
These entries do not support a prediction; the notes would have to carry written content before a direction could be read from them.
Alternatives to fastglm and ibis.iSDM
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 ibis.iSDM.
lavaanExtra
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
anyflights
A nycflights13 generator whose recent work is all about the data being right.
vinereg
Conditional density and log-likelihood fill out a vine copula regression package.
stringx
A drop-in string API for base R, kept alive by upstream check failures.
healthyR.data
From a bundled hospital dataset to a live CMS API client.
See all fastglm alternatives → · See all ibis.iSDM alternatives →
Recent activity from fastglm and ibis.iSDM
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agofastglmFirth generalised to all families, plus SQUAREM and stability fixes
- 3mo agofastglmCRAN release 0.1.0 ⚡
- 2y agoibis.iSDMVersion 0.1.1
- 3y agoibis.iSDMVersion 0.0.7
- 3y agoibis.iSDMVersion 0.0.6
- 3y agoibis.iSDMraster replaced by terra across the package ⚡
- 4y agofastglmC++ headers exposed for linking
- 7y agofastglmFirst CRAN release of the C++ IRLS solver
Frequently asked questions
What is the difference between fastglm and ibis.iSDM?
Both compete on the same themes — r-package — within Analytics. fastglm and ibis.iSDM 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 ibis.iSDM?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fastglm and ibis.iSDM 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 ibis.iSDM?
Top ibis.iSDM alternatives in Analytics are ranked by recent ship velocity. Browse the "ibis.iSDM alternatives" section above for the current picks, or visit /alternatives/ibis-isdm for the full list with editorial commentary on each.