NEWS.md

In glmbayes: Bayesian Generalized Linear Models (IID Samples)

glmbayes 0.9.75

Bug fixes

• Configure (Linux/macOS): -DUSE_OPENCL is set only when a non-PoCL OpenCL platform exposes at least one GPU device. PoCL-only ICD stacks (typical on CRAN debian-gcc incoming) no longer enable OpenCL at compile time, so has_opencl() is FALSE during check and kernel builds do not touch ~/.cache/pocl (addresses incoming NOTE on tempfile_* there). Real GPU installs (NVIDIA/AMD/Intel) are unchanged.

• Vignette Chapter 16: diagnose_glmbayes(), opencltools::diagnose_glmbayes(), and example(Cleveland) chunks are eval=FALSE during vignette rebuild (defense in depth alongside configure).

glmbayes 0.9.74

Bug fixes

• OpenCL examples: Cleveland, Boston_centered, and gpu_diagnostics kernel-load examples run OpenCL code only when NOT_CRAN="true" and has_opencl() (same policy as OpenCL testthat skips on CRAN). During CRAN checks they print a skip message instead of calling opencltools / GPU paths (avoids PoCL cache NOTEs on incoming Linux builders when OpenCL is compiled in).

glmbayes 0.9.73

Bug fixes and Removal of any external recommendation for Github install

• Configure: Removed tools/rcpp_include.R Rcpp header probing, Function.h branch flags, and glmbayes_getRegisteredNamespace shim on Unix and Windows (rely on LinkingTo: Rcpp and CRAN Rcpp instead).

glmbayes 0.9.72

Bug fixes

• Examples: Prior_Setup, pfamily, and simfuncs examples use use_parallel = FALSE during CRAN checks.

glmbayes 0.9.71

Bug fixes

• configure.win: Windows builds now set -DRCPP_PARALLEL_USE_TBB=1 and append RcppParallel::RcppParallelLibs() to PKG_LIBS, matching updated CRAN/R-Universe RcppParallel TBB linking requirements.

glmbayes 0.9.7

Highlights

• CRAN dependency on nmathopencl: OpenCL statistical kernels are now supplied by the nmathopencl package (hard Imports dependency), including Windows binaries on CRAN — unblocking GPU/OpenCL builds on Windows without vendored nmath in glmbayes.
• Bug fixes (see below): print.lmb() call display, truncated dIndependent_Normal_Gamma dispersion prior simulation for simulate_prior(), and related documentation/roxygen cleanup.

insight integration

• Added S3 methods for insight accessors on glmb fits (model_info, get_parameters, find_parameters, find_algorithm, get_data, get_priors). insight moves from Suggests to Imports and key generics are re-exported. get_priors() returns pfamily(model) (full prior specification, including complete Sigma) rather than a marginal-only table.

bayestestR prior-checking integration

• Added simulate_prior(), check_prior(), and describe_prior() methods for glmb fits; bayestestR moves from Suggests to Imports. Each pfamily() stores a matching pfun (like simfun); simulate_prior.glmb() calls it, including the truncated inverse-gamma dispersion prior used by dIndependent_Normal_Gamma() fits.

Bug fixes

• print.lmb(): Single-response lmb() fits again show a concise Call: line (formula, n, data, and related arguments) instead of deparsing the full pfamily object, including embedded simfun source. When multi-response lmb() / mlmb support was added in 0.9.6, .mlmb_lmb_display_call() omitted pfamily from the stored call for each block fit only; univariate lmb() still used the internal .uni_lmb() matched call via do.call(), which inlined the evaluated pfamily. Univariate fits now use the same display-call helper as mlmb.

Independent Normal-Gamma simulation

• Updates to independent normal gamma simulation to better handle non-isotropic priors with highly differentiated implied pweights across dimensions.

CPU nmath phase-out

• Removed vendored CPU R Mathlib sources; CPU routines in C++ now use R's libR via <Rmath.h>.

OpenCL kernel loading

• OpenCL nmath comes from nmathopencl; glmbayes likelihood/envelope kernels remain under inst/cl/, loaded via opencltools.

glmbayes 0.9.6

Highlights

• Multi-response lmb(): lmb() now handles both univariate and multivariate responses with a single unified interface, mirroring the behaviour of R's lm(). When the response has a single column the result is an lmb object (unchanged from prior releases). When the formula specifies multiple response columns (e.g. cbind(y1, y2) ~ x), lmb() fits a separate Bayesian linear model per response column and returns a named list with class mlmb. For the multi-response case, pfamily must be a list of pfamily objects with exactly one entry per response column; passing a single pfamily object is an error. Summary, print, and coefficient methods for mlmb objects are included.

• Conjugate GLM priors (Poisson, binomial, Gamma): New closed-form IID sampling paths for intercept-only models with identity links. dBeta() with rBeta_reg() supports Beta–Binomial(identity) conjugate updates; dGamma(Inv_Dispersion = FALSE) with rGamma_Conjugate_reg() supports Gamma–Poisson(identity) and Gamma–Gamma(identity) rate priors. Prior_Setup() can calibrate conjugate hyperparameters for these families (weighted Poisson rate and binomial probability defaults). See ?dBeta, ?dGamma, and the Chapter 02 / Chapter 07–11 vignettes.

• Vignette structure: Reworked Chapter 00 as a roadmap across five main parts plus technical appendices. Chapter 02 is now a conceptual introduction to single-parameter conjugacy; worked examples move to Chapter 02-S01 through Chapter 02-S05 (Beta–Binomial, Normal–Normal, Gamma–Poisson, exposure-weighted Poisson, and related topics). A Companion textbooks section in Chapter 00 indexes optional Bayes Rules! and LearnBayes appendices tied to the main GLM chapters.

• opencltools import: Core host/runtime OpenCL discovery and diagnostics (detect_*, PATH helpers, environment checks) now live in the opencltools package (Imports, >= 0.8.0). glmbayes keeps package-specific entry points (has_opencl(), diagnose_glmbayes()) that report compile-time OpenCL status for this build while delegating shared GPU/runtime checks—reducing duplicated maintenance in glmbayes.

• Bayes Rules! companion examples: Optional vignette appendices reproduce book datasets and published posterior summaries using lmb(), glmb(), Prior_Setup(), and dNormal() (suggested package bayesrules for data only). Coverage includes bikes (Ch. 03), weather_perth (Ch. 08–09), equality_index (Ch. 10), Gamma–Poisson conjugacy (Ch. 02-S04), and a scope note for Gamma regression (Ch. 11). Comparison tables use printed book values, not live rstanarm fits. See Chapter 00 § Companion textbooks.

• LearnBayes examples: Chapter 02-S04, Appendix A, maps the hearttransplants example from Albert (2009) / LearnBayes (exposure-weighted Gamma–Poisson conjugacy) to glmb() with analytic Albert posteriors for verification (suggested package LearnBayes).

Other changes

• Prior-vs-data guard for dIndependent_Normal_Gamma sampling: rindepNormalGamma_reg() now rejects calls where the Gamma (precision) part of the prior carries more effective prior observations than the data supply: inverting the Prior_Setup() calibration shape = (n_prior + 1 + p)/2, sampling requires n_prior <= n_w = sum(weights) (equivalently a prior weight pwt <= 0.5). Rationale: the dispersion envelope caps its log-tilt at n_w/2 - the data contribution to the posterior Gamma shape (Remark 4.1.3 of the ING vignette) - a strengthening of the validity condition lm_log2 < shape2 that presumes a likelihood-dominated regime. Prior-dominated calls could previously bind that cap on every envelope build (console UB3A mean slope warnings) and silently degrade the envelope. Note that n_prior here is the effective sample size of the Gamma component specifically; under the Prior_Setup() calibration the Gamma and coefficient parts share a common n_prior, so the two are not fully independent.

• Expanded testthat coverage for dBeta() / binomial(identity) conjugate paths and related glmb() integration.

glmbayes 0.9.5

• Tests / CRAN: All OpenCL-specific testthat blocks now call skip_on_cran() (in addition to skip_if_no_opencl()), consistent with existing Boston/Cleveland OpenCL tests. OpenCL coverage remains for local checks and source builds with OpenCL; CRAN checks avoid parallel/GPU-heavy tests that could trigger CPU time vs elapsed time NOTES.

glmbayes 0.9.4

• Vignettes: A vignette that previously used the notangle engine now uses the standard R Markdown vignette machinery (knitr / rmarkdown::html_vignette), so builds align with CRAN expectations and vignette index ordering should be consistent with the rest of the package.

• OpenCL sources (inst/cl): Removed unused or superseded material, consolidated kernels and library fragments, and aligned .cl layout and dependency tagging with the conventions used in 'openclport' and 'nmathopencl' (prelude, shims, nmath/ stems, family kernels under src/). See inst/cl/README.md for how the assembled program is stitched.

• OpenCL program assembly: Reworked loading so the full OpenCL program is built from explicit fragments (global header, nmath closure, family/link kernels) rather than ad hoc concatenation—clearer ownership of what enters GPU compilation and easier parity with CPU paths.

• Tests: Added and expanded testthat coverage aimed at OpenCL code paths (including binomial examples that exercise GPU envelope evaluation), complementing existing Cleveland-style checks.

• Bug fix — binomial OpenCL: Binomial f2_f3 OpenCL kernels now evaluate the data log-likelihood with the same proportion × trial-count semantics as dbinom_glmb on the CPU (round successes and trials, clamped probability). This fixes envelope / PLSD failures for aggregated binomial data (e.g. cbind(successes, failures) / MASS::menarche) where the previous kernels treated y like a raw success count.

glmbayes 0.9.3

• Published on CRAN.
• Version bump in response to CRAN resubmission feedback.

glmbayes 0.9.2

• Version bump in preparation for resubmission incorporating CRAN review feedback.

glmbayes 0.9.1

• Wrapped OpenCL-dependent examples in \donttest{} for CRAN compliance.
• Reduced iteration counts in rlmb Gibbs sampler example to stay within CRAN example time limits on slower check machines.

glmbayes 0.9.0

First CRAN submission. This release is a stable pre-release with a near-complete feature set relative to earlier development builds.

Highlights

Bayesian Generalized Linear (glmb) and Linear (lmb) modeling functions:

glmb() is a Bayesian analog for the classical glm() function while lmb() covers Gaussian models. Calls largely mirror those for the classical functions but leverage pfamilies for prior specifications. Method functions largely mirror those for the classical functions. Samples generated by the functions are largely iid samples (no MCMC convergence dignostics are needed).

Implemented Likelihood families/ link functions:

Most of the families implemented in the glm() function are also implemented in the glmb() function (the lmb() function covers only gaussian() families). Link functions that lead to log-concave likelihood functions are generally implemented. Specifically, we have the following:

Supported likelihoods: gaussian (identity), Poisson / quasi-Poisson (log), binomial / quasi-binomial (logit, probit, cloglog), Gamma (log).

Prior Family functions:

pfamily constructors are used to specify priors and play the same kind of role for the prior specifications as family constructors and link functions play for the likelihoods. Specifically, we have the following:

Supported Priors: Normal (all families/links), Normal–Gamma and independent Normal–Gamma (gaussian families), and Gamma-on-precision (gaussian and Gamma families).

Prior_Setup function:

The package comes with a convenient Prior_Setup() function that provides default prior input parameters for each of the implemented models. Basic calls (without tailoring) mirror traditional calls to the glmb() and lmb() functions respectively and only require the user to provide the model formula and (if not the gaussian family) the family/link function.

The function can also be used to easily adjust prior specifications (see documentation for details).

Extensive Method functions:

The package comes with extensive method functions that mirror those for the classical functions. These include dedicated print(), summary(), predict() and simulate() functions.

Lower Level Modeling functions:

The package comes with lower level modeling/simulation functions that advanced users can use to implement block Gibbs samplers. These generally come with less overhead than the glmb() and lmb() functions and are called internally by the the higher level modeling functions.

RcppParallel and OpenCL GPU Acceleration Implementations

Some of the simulation functions comes with use_parallel and use_opencl options that speed up simulation for higher dimensional models.

Extensive help files, vignettes, examples and demos

The package also comes with extensive help files for the varios functions that are complemented with a rich set of vignettes. A large number of examples and demos are also availabel (see the READM.md file for a sample).

Earlier development history (0.1.x series)

The notes below summarize major work during the initial development series before the 0.9.0 pre-release.

OpenCL and GPU acceleration

• Completed the OpenCL-based grid construction framework for large models.
• Added GPU-aware envelope sizing and improved OpenCL failure handling.
• Introduced diagnostic utilities to assess OpenCL availability and performance.
• Improved configure scripts to detect OpenCL and provide informative messages.
• Expanded OpenCL documentation and added a dedicated vignette chapter.

Parallel CPU sampling (RcppParallel)

• Enabled parallel envelope construction and parallel iid sampling.
• Added pilot functions for large-dimension grid estimation.
• Implemented thread-safe parallel sampling for independent normal-gamma models.

Core statistical improvements

• Migrated to an improved independent normal-gamma simulation algorithm.
• Added theoretical derivations for independent normal-gamma regression.
• Improved UB2 and RSS minimization routines, including scaling corrections.
• Enhanced Prior_Setup() to support family-specific prior construction.
• Added dedicated envelope evaluation and sizing functions.

Package infrastructure

• Significant cleanup to remove NOTES and improve CRAN readiness.
• Improved configure and Makevars files for portability.
• Added testthat tests, including OpenCL-specific tests.
• Consolidated envelope-building functions into a cleaner structure.

Documentation

• Major updates to README and package-level documentation.
• Added multiple new vignettes and expanded existing ones.
• Improved examples for lmb(), rlmb(), and OpenCL models.

Bug fixes (0.1.x era)

• Corrected scaling in UB2 minimization.
• Improved error handling for missing OpenCL functionality.
• Fixed various small issues uncovered during parallelization work.

glmbayes documentation built on Aug. 5, 2026, 1:07 a.m.