treasury
A thin Treasury rates wrapper has stopped adding endpoints and started making its tables self-describing.
Comparison · Analytics
glmbayes vs medrobust
A side-by-side editorial comparison of glmbayes and medrobust — release velocity, themes, recent moves, and the top alternatives to consider.
glmbayes vs medrobust: at a glance
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.
What is medrobust?
medrobust made its partial-identification bounds usable by giving them confidence intervals.
medrobust computes partial-identification bounds for mediation effects when exposure or mediator is differentially misclassified, part of the Data-Wise mediationverse. Its 0.2.0 release corrected three estimator defects against population oracles and added Imbens-Manski confidence intervals for the bounds; the two releases since have paired each identification path with a real public-domain dataset and a worked vignette. CRAN is deferred, with distribution through GitHub and r-universe.
glmbayes vs medrobust: editorial side-by-side
glmbayes
6.3ANALYTICS
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.
medrobust
0.0ANALYTICS
medrobust made its partial-identification bounds usable by giving them confidence intervals.
◆ Current state
medrobust computes partial-identification bounds for mediation effects when exposure or mediator is differentially misclassified, part of the Data-Wise mediationverse. Its 0.2.0 release corrected three estimator defects against population oracles and added Imbens-Manski confidence intervals for the bounds; the two releases since have paired each identification path with a real public-domain dataset and a worked vignette. CRAN is deferred, with distribution through GitHub and r-universe.
◆ Where it's heading
The pattern is deliberate and symmetric: 0.3.0 shipped the mediator-side example on NCHS natality data, 0.4.0 its exposure-side mirror on NHANES, each demonstrating what the bounds do when reporting accuracy is allowed to depend on the outcome. Alongside that runs a consistent concern with failing usefully rather than loudly — bound_ne() returns NA bounds with a machine-readable reason and a typed condition instead of aborting, so a simulation replicate is recorded rather than lost, and non-finite endpoint standard errors produce a documented NA rather than a silent one. That is a package expecting to be run thousands of times inside someone else's loop.
◆ Prediction
Both identification paths now have a dataset, a vignette and interval coverage, so the next release is most likely the deferred CRAN submission rather than new methodology.
Alternatives to glmbayes and medrobust
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 medrobust.
soilDB
The R front door to USDA soil data finishes a long deprecation cleanup and turns local-first.
lstar
A single-cell data store commits to Zarr v3 and range-readable hosting across four language surfaces
quanteda
Text analysis in R keeps optimising its token internals — and builds a path out to torch
moderndive
The ModernDive teaching package learns to render inside the browser that runs its own textbook
hydroloom
USGS puts a type system over its river network toolkit so errors surface at dispatch
See all glmbayes alternatives → · See all medrobust alternatives →
Recent activity from glmbayes and medrobust
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 10d agoglmbayesBack on CRAN after a configure policy fix
- 22d agoglmbayesOpenCL split out to nmathopencl; insight and bayestestR integration ⚡
- 1mo agoglmbayesMulti-response models and conjugate GLM priors
- 2mo agomedrobustNHANES exposure-side misclassification example dataset
- 2mo agomedrobustNatality example dataset; bounds degrade instead of aborting
- 2mo agomedrobustBounds corrected against oracles; Imbens-Manski intervals added ⚡
- 3mo agoglmbayesOpenCL kernels restructured and a binomial GPU bug fixed
- 3mo agoglmbayesVersion bump for CRAN resubmission
- 1y agoglmbayesCRAN-ready beta with the core S3 interface
Frequently asked questions
What is the difference between glmbayes and medrobust?
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 medrobust?
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 medrobust?
Top medrobust alternatives in Analytics are ranked by recent ship velocity. Browse the "medrobust alternatives" section above for the current picks, or visit /alternatives/medrobust for the full list with editorial commentary on each.