fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of brglm2 and vahtian — release velocity, themes, recent moves, and the top alternatives to consider.
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
A provenance-first corpus tool hands its verification core to agents over MCP
vahtian freezes a set of research records into a content-hashed, date-locked corpus, verifies it is untampered, and keeps a hash-chained audit ledger. It ships in Python and R with byte-identical content hashes enforced by a golden-hash test in both suites. In five weeks it went from first release to exposing its five core operations through a local stdio MCP server and registering in the MCP Registry.
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
The package's older work assumed the classical regime where observations comfortably outnumber parameters. mdyplFit() and its hd_correction argument target the opposite case, and the follow-up releases are almost entirely about it — Pearson residuals on original responses, aliased parameter handling, the sloe() signal-strength estimator ignoring leverage-one observations. Meanwhile the older surface gets graceful-failure work: brglm_fit() now returns its latest estimates with warnings rather than aborting.
Given that 1.0.1 and 1.1.0 are both dominated by mdyplFit follow-ups while the classical path receives only robustness fixes, further work on high-dimensional corrections is the likeliest direction.
vahtian freezes a set of research records into a content-hashed, date-locked corpus, verifies it is untampered, and keeps a hash-chained audit ledger. It ships in Python and R with byte-identical content hashes enforced by a golden-hash test in both suites. In five weeks it went from first release to exposing its five core operations through a local stdio MCP server and registering in the MCP Registry.
The direction is explicit in the project's own framing — human-first, AI-second, auditable — and the MCP server is what makes that framing operational rather than rhetorical. Rather than adding judgement, the tool is being positioned as the thing an agent calls to prove a corpus has not moved. The CiteVahti claim-source comparator, mirrored across both languages under a parity gate, extends the same idea to per-claim checking. Everything stays on the user's machine: no accounts, no telemetry.
The comparator's per-field epistemic states are the newest and least settled piece; expect the next release to extend those states or to widen the R package's distribution, which is still described as coming.
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 brglm2 or vahtian.
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 brglm2 alternatives → · See all vahtian alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. vahtian is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. 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. vahtian is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top brglm2 alternatives in Analytics are ranked by recent ship velocity. Browse the "brglm2 alternatives" section above for the current picks, or visit /alternatives/brglm2 for the full list with editorial commentary on each.
Top vahtian alternatives in Analytics are ranked by recent ship velocity. Browse the "vahtian alternatives" section above for the current picks, or visit /alternatives/vahtian for the full list with editorial commentary on each.