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glmbayes vs probmed

A side-by-side editorial comparison of glmbayes and probmed — release velocity, themes, recent moves, and the top alternatives to consider.

glmbayes vs probmed: at a glance

Featureglmbayesprobmed
SectorAnalyticsAnalytics
Velocity score6.30.0
Sparks · 30d10
Top themesbayesian-statistics, generalized-linear-models, opencl, r-packagecausal mediation, effect size, semiparametric inference, cross-fitting
Last editorial update50m ago1h ago
WebsiteVisit →Visit →

What is glmbayes?

A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain

glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.

Read the full glmbayes trajectory →

What is probmed?

probmed went from one probabilistic effect size to a family of them in sixteen days.

probmed computes P_med, a scale-free probabilistic effect size for causal mediation, as part of the Data-Wise mediationverse alongside medfit, medsim and RMediation. Three releases in three weeks took it from a single estimator to four additional families built on a shared cross-fitted corner-EIF core, covering gauge-calibrated, incremental-elasticity and Sobol variance-share versions of the proportion mediated. Distribution is GitHub and r-universe rather than CRAN, with a load-bearing Remotes pin on medfit.

Read the full probmed trajectory →

glmbayes vs probmed: editorial side-by-side

G
glmbayes
ANALYTICS
6.3

A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain

◆ Current state

glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.

◆ Where it's heading

The arc is about removing reasons not to use it. GPU support was previously blocked on Windows because the OpenCL kernels were vendored; splitting them into a CRAN package with binaries fixed that. The ecosystem work does the same thing for tooling — a glmb fit now responds to get_parameters, get_priors, simulate_prior and check_prior, so it drops into workflows built around easystats rather than requiring its own. The CRAN archival and the configure fixes that followed show how much of the effort goes into distribution rather than modelling.

◆ Prediction

get_priors() returning the full prior specification rather than a marginal table is the kind of detail that invites further bayestestR integration, and the diagnostic surface is the least built-out part of what has shipped so far.

P
probmed
ANALYTICS
0.0

probmed went from one probabilistic effect size to a family of them in sixteen days.

◆ Current state

probmed computes P_med, a scale-free probabilistic effect size for causal mediation, as part of the Data-Wise mediationverse alongside medfit, medsim and RMediation. Three releases in three weeks took it from a single estimator to four additional families built on a shared cross-fitted corner-EIF core, covering gauge-calibrated, incremental-elasticity and Sobol variance-share versions of the proportion mediated. Distribution is GitHub and r-universe rather than CRAN, with a load-bearing Remotes pin on medfit.

◆ Where it's heading

The pace is manuscript-driven — estimators arrive with their citations attached and vignettes alongside, and the 0.1.0 notes correct the estimand itself against a manuscript definition rather than fixing a bug in code. Each release adds inference machinery as well as point estimates: percentile-bootstrap intervals and Fieller sets in 0.3.0, a deterministic MBCO interval in 0.2.0 that avoids resampling entirely. The gauge residual and the pmed_sensitivity() helper suggest a growing concern with when the estimand does not decompose at all.

◆ Prediction

0.3.0 shipped a sensitivity helper for shared mediator-outcome confounding and a diagnostic that flags non-decomposability, so the next release most likely extends that diagnostic side rather than adding a fifth estimator family.

Alternatives to glmbayes and probmed

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 glmbayes or probmed.

See all glmbayes alternatives → · See all probmed alternatives →

Recent activity from glmbayes and probmed

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 10d agoglmbayesBack on CRAN after a configure policy fix
  2. 22d agoglmbayesOpenCL split out to nmathopencl; insight and bayestestR integration
  3. 1mo agoprobmedFour estimator families on a cross-fitted corner-EIF core
  4. 1mo agoglmbayesMulti-response models and conjugate GLM priors
  5. 2mo agoprobmedParallel mediators and a resampling-free MBCO interval
  6. 2mo agoprobmedFirst release: pmed() with the estimand corrected
  7. 3mo agoglmbayesOpenCL kernels restructured and a binomial GPU bug fixed
  8. 3mo agoglmbayesVersion bump for CRAN resubmission
  9. 1y agoglmbayesCRAN-ready beta with the core S3 interface

Frequently asked questions

What is the difference between glmbayes and probmed?

They serve adjacent needs but don't currently overlap on shipped themes. glmbayes 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.

Is glmbayes better than probmed?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. glmbayes 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.

What are the best alternatives to glmbayes?

Top glmbayes alternatives in Analytics are ranked by recent ship velocity. Browse the "glmbayes alternatives" section above for the current picks, or visit /alternatives/glmbayes for the full list with editorial commentary on each.

What are the best alternatives to probmed?

Top probmed alternatives in Analytics are ranked by recent ship velocity. Browse the "probmed alternatives" section above for the current picks, or visit /alternatives/probmed for the full list with editorial commentary on each.