simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of fastglm and vinereg — 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.
Conditional density and log-likelihood fill out a vine copula regression package.
vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.
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.
vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.
Work has concentrated on evaluation rather than fitting: cll() in 0.9.0, pdf() in 0.10.0, and the discrete-variable correction in 0.11.0 all concern what can be computed from a model already fitted. Releases arrive in same-day pairs, and the notes are terse enough that 0.10.0 reuses 0.9.0's wording verbatim, describing pdf() with cll()'s sentence. Version floors also track the sibling packages - kde1d here, rvinecopulib in 0.8.3.
Given the shared release rhythm across the stack, the next entry is as likely to be a dependency-driven bump as a new function; the discrete-variable path is the one area these notes show as recently unstable.
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 vinereg.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
A drop-in string API for base R, kept alive by upstream check failures.
From a bundled hospital dataset to a live CMS API client.
See all fastglm alternatives → · See all vinereg alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fastglm and vinereg 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 vinereg 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 vinereg alternatives in Analytics are ranked by recent ship velocity. Browse the "vinereg alternatives" section above for the current picks, or visit /alternatives/vinereg for the full list with editorial commentary on each.