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).
dBeta() / binomial(identity) conjugate
paths and related glmb() integration.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.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.
\donttest{} for CRAN compliance.First CRAN submission. This release is a stable pre-release with a near-complete feature set relative to earlier development builds.
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).
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).
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).
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).
The package comes with extensive method functions that mirror those
for the classical functions. These include dedicated print(),
summary(), predict() and simulate() 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.
Some of the simulation functions comes with use_parallel and use_opencl options that speed up simulation for higher dimensional models.
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).
The notes below summarize major work during the initial development series before the 0.9.0 pre-release.
Prior_Setup() to support family-specific prior construction.lmb(), rlmb(), and OpenCL models.