simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of fastglm and gghighlight — 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.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
gghighlight adds one verb to ggplot2: highlight the series matching a predicate and grey out the rest, with unhighlighted_params controlling how the shadowed layer renders and calculate_per_facet deciding whether the predicate evaluates within facets. The API settled at 0.2.0; the 0.5.0 release supports ggplot2 v4.0 including its ink and paper theme elements, and finally deletes gghighlight_point() and gghighlight_line().
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.
gghighlight adds one verb to ggplot2: highlight the series matching a predicate and grey out the rest, with unhighlighted_params controlling how the shadowed layer renders and calculate_per_facet deciding whether the predicate evaluates within facets. The API settled at 0.2.0; the 0.5.0 release supports ggplot2 v4.0 including its ink and paper theme elements, and finally deletes gghighlight_point() and gghighlight_line().
Two threads run through the history. One is a slow deprecation, from soft-deprecating the geom-specific functions at 0.1.0, to defunct at 0.3.0, to removed at 0.5.0 — a five-year removal cycle. The other is compatibility work: purrr 1.0.0, dplyr's across() deprecation, ggplot2 3.4.0, then 4.0. Genuine feature additions are rare and small, with line_label_type at 0.4.0 the last one. Note that 0.3.2's notes restate 0.3.1's n() item, so adjacent tags here overlap rather than each describing distinct work.
The next release most likely absorbs further ggplot2 4.x changes, given that is what triggered the last three. Nothing in the entries points to a new highlighting capability.
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 gghighlight.
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.
Conditional density and log-likelihood fill out a vine copula regression package.
A drop-in string API for base R, kept alive by upstream check failures.
See all fastglm alternatives → · See all gghighlight alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fastglm and gghighlight 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 gghighlight 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 gghighlight alternatives in Analytics are ranked by recent ship velocity. Browse the "gghighlight alternatives" section above for the current picks, or visit /alternatives/gghighlight for the full list with editorial commentary on each.