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Comparison · Analytics
brglm2 vs cTMed
A side-by-side editorial comparison of brglm2 and cTMed — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
brglm2 vs cTMed: at a glance
What is brglm2?
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.
What is cTMed?
Continuous-time mediation effects get standardized centrality, six years into steady patch work
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
brglm2 vs cTMed: editorial side-by-side
brglm2
0.0ANALYTICS
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
cTMed
2.5ANALYTICS
Continuous-time mediation effects get standardized centrality, six years into steady patch work
◆ Current state
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
◆ Where it's heading
The package is filling out a matrix rather than changing shape: for each effect type there is a delta-method, a Monte Carlo and a bootstrap path, and each release closes another cell. The 2025 releases were largely externally forced — an Armadillo 15.0.x transition at CRAN, a citation addition after the Psychological Methods paper landed — which suggests the statistical core has been settled since the 1.0.6 standardization revision.
◆ Prediction
The diagonal-sigma option has now reached the standardized estimators; extending it to the remaining unstandardized variants is the obvious next cell to fill.
Alternatives to brglm2 and cTMed
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 cTMed.
gcube
gcube's recent releases are all packaging metadata, not simulation code
cubist
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats
ggstats keeps widening what a coefficient or Likert plot can be
ecodive
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
simStateSpace
State-space data simulation for R, filled in one function at a time
See all brglm2 alternatives → · See all cTMed alternatives →
Recent activity from brglm2 and cTMed
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 27d agocTMedStandardized centrality measures and diagonal-sigma support
- 3mo agobrglm2brglm2 v1.1.0
- 6mo agocTMedMinor method edits
- 8mo agobrglm2brglm2 v1.0.1
- 10mo agocTMedPackage citation added for the Psychological Methods paper
- 10mo agocTMedArmadillo 15.0.x compatibility for CRAN
- 11mo agobrglm21.0.0 adds maximum DY-prior penalized likelihood for logistic regression ⚡
- 1y agobrglm2brglm2 v0.9.3
- 1y agobrglm2brglm2 v0.9.2
- 1y agocTMedStandardization reworked around the steady-state covariance matrix
- 1y agocTMedBootstrap centrality estimators and MCPhiSigma()
- 3y agobrglm2brglm2 v0.9.1
Frequently asked questions
What is the difference between brglm2 and cTMed?
Both compete on the same themes — r-package — within Analytics. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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.
Is brglm2 better than cTMed?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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.
What are the best alternatives to brglm2?
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.
What are the best alternatives to cTMed?
Top cTMed alternatives in Analytics are ranked by recent ship velocity. Browse the "cTMed alternatives" section above for the current picks, or visit /alternatives/ctmed for the full list with editorial commentary on each.