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Comparison · Analytics
glmbayes vs susier
A side-by-side editorial comparison of glmbayes and susier — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
glmbayes vs susier: 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 susier?
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
susieR implements the Sum of Single Effects regression model for variable selection and fine-mapping, widely used in statistical genetics. The recent releases are a tight run of correctness work concentrated in one area: null effect trimming. Version 0.15.55 fixed trimming under the Servin-Stephens residual variance method, 0.15.56 fixed it again for non-uniform prior weights fourteen minutes later, 0.15.57 corrected an ELBO null space term for RSS with X and a matrix symmetry check, and 0.15.58 addressed an alpha0/beta0 issue. Version 0.16.0 migrates the C++ bindings from Rcpp to cpp11 with cpp11armadillo.
glmbayes vs susier: 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.
susier
0.0ANALYTICS
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
◆ Current state
susieR implements the Sum of Single Effects regression model for variable selection and fine-mapping, widely used in statistical genetics. The recent releases are a tight run of correctness work concentrated in one area: null effect trimming. Version 0.15.55 fixed trimming under the Servin-Stephens residual variance method, 0.15.56 fixed it again for non-uniform prior weights fourteen minutes later, 0.15.57 corrected an ELBO null space term for RSS with X and a matrix symmetry check, and 0.15.58 addressed an alpha0/beta0 issue. Version 0.16.0 migrates the C++ bindings from Rcpp to cpp11 with cpp11armadillo.
◆ Where it's heading
The version-number churn understates how narrow this work is — four consecutive releases touching the same trimming and residual-variance machinery suggests one area where the implementation and the intended behavior had drifted apart. The 0.16.0 binding migration is the only structural change, and it is invisible to users while mattering for build portability and long-term maintenance. Development is clearly active, with automated release tooling and dependency bumps flowing through the same stream.
◆ Prediction
With the binding migration just landed, near-term releases are likely to address fallout from it alongside continued fixes in the same trimming and residual-variance code.
Alternatives to glmbayes and susier
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 susier.
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 glmbayes alternatives → · See all susier alternatives →
Recent activity from glmbayes and susier
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11d 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
- 3mo agoglmbayesOpenCL kernels restructured and a binomial GPU bug fixed
- 3mo agoglmbayesVersion bump for CRAN resubmission
- 3mo agosusierMigrates C++ bindings from Rcpp to cpp11 and cpp11armadillo
- 3mo agosusierFixes alpha0/beta0 handling under Servin-Stephens
- 4mo agosusierCorrects the null space ELBO term for RSS with X
- 5mo agosusierFixes null effect trimming with non-uniform prior weights
- 5mo agosusierFixes null effect trimming under Servin-Stephens estimation
- 1y agoglmbayesCRAN-ready beta with the core S3 interface
Frequently asked questions
What is the difference between glmbayes and susier?
Both compete on the same themes — r-package — within Analytics. 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 susier?
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 susier?
Top susier alternatives in Analytics are ranked by recent ship velocity. Browse the "susier alternatives" section above for the current picks, or visit /alternatives/susier for the full list with editorial commentary on each.