rollama
rollama turns a local-LLM wrapper into an instrument for reproducible annotation
A side-by-side editorial comparison of brglm2 and ojoregex — 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.
Oklahoma's court-data nonprofit maintains the regex layer that turns charge text into categories.
ojoregex is Open Justice Oklahoma's pattern library for classifying criminal charge descriptions from court records — the unglamorous translation layer between free-text charge fields and analysable categories. Its entire release history reached this feed as four tags published within three minutes, so the feed order reflects a backfill rather than a shipping cadence. Release notes are merge references rather than descriptions, which limits how much can be read from the changelog alone.
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.
ojoregex is Open Justice Oklahoma's pattern library for classifying criminal charge descriptions from court records — the unglamorous translation layer between free-text charge fields and analysable categories. Its entire release history reached this feed as four tags published within three minutes, so the feed order reflects a backfill rather than a shipping cadence. Release notes are merge references rather than descriptions, which limits how much can be read from the changelog alone.
What the notes do show is a package alternating between domain corrections and R tooling upkeep: a fix to property-crime matching in one release, dplyr select semantics in the next. That is the expected shape for a regex catalogue — accuracy work arrives as individual charge types get miscategorised in real analyses, and the rest is keeping the package installable against a moving tidyverse. Contributions come from a small internal team, and the vignette work referenced in the earliest tag suggests the pattern list doubles as documentation for analysts.
The visible pattern is incremental match fixes as charge types surface in use; the release notes carry too little detail to predict anything beyond that without reading the underlying pull requests.
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 ojoregex.
rollama turns a local-LLM wrapper into an instrument for reproducible annotation
A ggplot2 inset-map extension that is now infrastructure for other packages
hoopR rebuilds its HTTP layer on httr2 to stop segfaulting on modern systems
NSW boundary data for R, refreshed as the official sources move
Biodiversity impact indicators settle their vocabulary before 1.0
A dormant trajectory-inference wrapper wakes up for maintenance only
See all brglm2 alternatives → · See all ojoregex alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. brglm2 and ojoregex 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. brglm2 and ojoregex 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 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 ojoregex alternatives in Analytics are ranked by recent ship velocity. Browse the "ojoregex alternatives" section above for the current picks, or visit /alternatives/ojoregex for the full list with editorial commentary on each.