fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of brglm2 and medsim — 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.
medsim is turning simulation runs into auditable artifacts, not just fast ones.
medsim is a young Monte Carlo harness for mediation-analysis simulation studies, first tagged in May 2026 and already at 0.5.1. The last two releases moved the package's center of gravity from running simulations to proving a run is trustworthy: chunk provenance headers, a single-SHA assertion across chunks, and a pilot-subset positive control. The statistical work sits in the missing-data line added in 0.2.0 — Fleishman non-normal generators, rate-calibrated MCAR/MAR/MNAR amputation, and a validated D4-stacked MBCO estimator.
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.
medsim is a young Monte Carlo harness for mediation-analysis simulation studies, first tagged in May 2026 and already at 0.5.1. The last two releases moved the package's center of gravity from running simulations to proving a run is trustworthy: chunk provenance headers, a single-SHA assertion across chunks, and a pilot-subset positive control. The statistical work sits in the missing-data line added in 0.2.0 — Fleishman non-normal generators, rate-calibrated MCAR/MAR/MNAR amputation, and a validated D4-stacked MBCO estimator.
The arc is toward defensible HPC runs: each 0.5.x gate closes a way a cluster job could silently produce wrong output, and 0.5.1 extends the same suspicion to the estimator itself by exposing the branch disagreement the standard ARIV averages away. Releases are cadenced against discovered defects rather than a roadmap — 0.5.0 cites seven findings from a pre-integration review, and 0.5.1 cites an adversarial review of 0.5.0. The audit surface is widening faster than the method surface.
The collapse-audit exclusion list has now been patched twice for method-specific diagnostic columns, so the next likely move is a contract letting methods declare their own discrete fields instead of medsim naming them centrally.
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 medsim.
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 medsim alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. medsim is currently shipping more aggressively (velocity 6.3 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. medsim is currently shipping more aggressively (velocity 6.3 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 medsim alternatives in Analytics are ranked by recent ship velocity. Browse the "medsim alternatives" section above for the current picks, or visit /alternatives/medsim for the full list with editorial commentary on each.