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glmbayes - Bayesian Generalized Linear Models (IID Samples)

Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models.

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8.91 score 5 stars 1 dependents 44 scripts 391 downloads

nmathopencl - 'OpenCL'-Ported R 'Mathlib' for GPU-Accelerated Packages

Ships statistical and mathematical routines from R internal 'nmath' ('Mathlib') as 'OpenCL' C sources under directory 'inst/cl/', with R wrappers that use the GPU when 'OpenCL' is available at compile time and fall back to 'stats' equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs such as package 'glmbayes') using 'opencltools' kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the 'nmath' ('Mathlib') and 'Rmath' sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" <doi:10.32614/R.manuals>. 'OpenCL' GPU execution follows the standard described in Stone, Gohara, and Shi (2010) <doi:10.1109/MCSE.2010.69>. The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>.

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openblascpp

8.48 score 5 stars 4 dependents 339 downloads

glmbayesCore - Core C++ Sampling Engine for 'glmbayes'

Core C++ engine for 'glmbayes': envelope-based iid linear and generalized linear model samplers, prior-family routing, and optional 'OpenCL' acceleration. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. Intended as a developer backend for the 'glmbayes' formula interface; end users should use 'glmbayes' for modelling with interfaces analogous to 'lm' and 'glm'. Experimental mixed-model design utilities use the 'lme4', 'Matrix', 'reformulas', and 'glmmTMB' packages (API only; no bundled third-party source).

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openblasonetbbcpp

8.28 score 3 stars 3 dependents 348 downloads

opencltools - 'OpenCL' Tools for R Package Developers

Runtime 'OpenCL' support for R package developers: probe hardware and drivers, load and concatenate kernel sources, and manage dependency-annotated '.cl' libraries, so packages like 'nmathopencl' and other ported libraries can offer GPU acceleration without each re-implementing and related helpers. Vignettes illustrate integration with suggested package 'nmathopencl' and with downstream applications such as 'glmbayes'; production kernels for those applications ship in those packages rather than here.

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cpp

7.95 score 5 stars 5 dependents 1 scripts 283 downloads

lmebayesCore - Core C++ Sampling Engine for 'lmebayes'

Core C++ engine for lmebayes: envelope-based iid samplers, two-block Gibbs mixed-model engines, and optional OpenCL acceleration. Full-featured developer backend for lmebayes and extensions. End users should use lmebayes for lmer/glmer-style mixed-model workflows.

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openblasonetbbcpp

6.34 score 1 dependents

lmebayes - Bayesian Linear Mixed-Effects Models via Two-Block Gibbs Sampling

Provides Bayesian linear and generalized linear mixed-effects model fitting with near-independent posterior samples. The main functions mirror the 'lme4' lmer() and glmer() interfaces (X/beta for fixed effects, Z/b for random effects, D for the random-effects covariance) while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling uses efficient two-block Gibbs sampling procedures with parallel chains and theoretically driven stopping rules. Gibbs sampling engines are provided by the 'lmebayesCore' package.

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cpp

5.79 score