Package: glmbayesCore 0.1.0

glmbayesCore: Core C++ Sampling Engine for glmbayes

Core C++ engine for glmbayes: envelope-based iid samplers, Gibbs building blocks, and optional OpenCL. Developer backend for glmbayes, lmebayes, and extensions. End users should use glmbayes for lm/glm modelling.

Authors:Kjell Nygren [aut, cre], The R Core Team [ctb, cph], The R Foundation [cph], Ross Ihaka [ctb, cph], Robert Gentleman [ctb, cph], Simon Davies [ctb], Morten Welinder [ctb, cph], Martin Maechler [ctb]

glmbayesCore_0.1.0.tar.gz
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glmbayesCore_0.1.0.tgz(r-4.6-x86_64)glmbayesCore_0.1.0.tgz(r-4.6-arm64)glmbayesCore_0.1.0.tgz(r-4.5-x86_64)glmbayesCore_0.1.0.tgz(r-4.5-arm64)
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manual.pdf |manual.html✨
card.svg |card.png
glmbayesCore/json (API)
NEWS

# Install 'glmbayesCore' in R:
install.packages('glmbayesCore', repos = c('https://knygren.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/knygren/glmbayescore/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • AMI - Amitriptyline overdose data
  • BikeSharing - Bike Sharing Dataset
  • Boston_centered - Boston housing data with mean-centered predictors
  • carinsca - Canadian Automobile Insurance Claims for 1957-1958
  • Cleveland - Cleveland Heart Disease Dataset

On CRAN:

Conda:

openblascpp

3.97 score 2 stars 60 exports 9 dependencies

Last updated from:3df73962ba. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK391
linux-devel-x86_64OK370
source / vignettesOK631
linux-release-arm64OK419
linux-release-x86_64OK359
macos-release-arm64OK247
macos-release-x86_64OK428
macos-oldrel-arm64OK200
macos-oldrel-x86_64OK500
windows-develOK492
windows-releaseOK502
windows-oldrelOK282
wasm-releaseFAIL305

Exports:block_rNormalGLMblock_rNormalGLM_updateblock_rNormalRegblock_rNormalReg_updatebuild_mu_allcompute_gaussian_priordBetadGammadiagnose_glmbayesdIndependent_Normal_GammadNormaldNormal_GammaEnvelopeBuildEnvelopeCenteringEnvelopeDispersionBuildEnvelopeEvalEnvelopeOptEnvelopeOrchestratorEnvelopeSetGridEnvelopeSetLogPEnvelopeSizeEnvelopeSortglmb_Standardize_Modelglmb.wfitglmbayesCore_has_openclglmbfamfuncglmerb_posterior_modelmerb_posterior_meanmulti_prior_setupmulti_rNormal_regnormalize_blockpfamilypfamily_listpinvgamma_ctpnorm_ctPrior_CheckPrior_Setupqinvgamma_ctrBeta_regrGamma_Conjugate_regrgamma_ctrGamma_regrglmbrindepNormalGamma_regrIndepNormalGammaReg_stdrinvgamma_ctrlmbrnorm_ctrNormal_regrNormal_reg.wfitrNormalGamma_regrNormalGLM_stdsimfunctiontwo_block_l_for_tvtwo_block_mode_weightstwo_block_ratetwo_block_rate_v2two_block_rNormal_regtwo_block_rNormal_reg_v2two_block_tv_bound

Dependencies:jsonliteMASSnmathopenclopencltoolsrbibutilsRcppRcppArmadilloRcppParallelRdpack

Readme and manuals

Help Manual

Help pageTopics
glmbayesCore: Core C++ Sampling Engine for glmbayesglmbayesCore-package glmbayesCore
Amitriptyline overdose dataAMI
Bike Sharing Dataset (Processed)BikeSharing
One Gibbs block update via 'block_rNormalGLM'block_rNormalGLM block_rNormalGLM_update block_rNormalReg block_rNormalReg_update block_simfuncs
Boston housing data with mean-centered predictorsBoston_centered
Build per-group random-effect prior means for Block 1 samplingbuild_mu_all
Canadian Automobile Insurance Claims for 1957-1958carinsca
Cleveland Heart Disease DatasetCleveland
Compute Calibrated Gaussian Normal–Gamma Prior Componentscompute_gaussian_prior
GPU and OpenCL diagnostics for 'glmbayes'diagnose_glmbayes glmbayesCore_has_opencl gpu_diagnostics
GPU-Accelerated Envelope Construction for Posterior SimulationEnvelopeBuild EnvelopeSetGrid EnvelopeSetLogP
Envelope Centering for Bayesian Gaussian RegressionEnvelopeCentering
Builds Dispersion-Aware Envelope for SimulationEnvelopeDispersionBuild
Evaluate Negative Log-Likelihood and GradientsEnvelopeEval
Envelope Construction Orchestrator for Bayesian Gaussian RegressionEnvelopeOrchestrator
Envelope Sizing and OptimizationEnvelopeOpt EnvelopeSize
Sorts Envelope function for simulationEnvelopeSort
Model Formulae for 'summary.rglmb' Objectsformula.summary.rglmb
The Central Gamma Distributionctrgamma Gamma_ct pgamma_ct rgamma_ct
Standardize A Non-Gaussian Modelglmb_Standardize_Model
Return family functions used during simulation and post processingglmbfamfunc print.glmbfamfunc
Joint posterior mode of the two-block GLMM (Gaussian case)glmerb_posterior_mode
The Central Inverse-Gamma DistributionInvGamma_ct pinvgamma_ct qinvgamma_ct rinvgamma_ct
Joint posterior mean of the two-block Gaussian modellmerb_posterior_mean
Prior setup for multiple Gaussian responsesmulti_prior_setup
Multi-response Normal regression simulationmulti_rNormal_reg
The Central Normal DistributionNormal_ct pnorm_ct rnorm_ct
Normalize a row-block partition for BY-style fitsnormalize_block
Prior Family Objects for Bayesian ModelsdBeta dGamma dIndependent_Normal_Gamma dNormal dNormal_Gamma pfamily print.pfamily
Build a named list of pfamily objectspfamily_list
Print method for two_block_mode_weights objectsprint.two_block_mode_weights
Print method for two_block_rate objectsprint.two_block_rate
Checks for Prior-data conflictsPrior_Check
Setup Prior Objectsprint.PriorSetup Prior_Setup
Deviance Residuals for 'rglmb' and 'summary.rglmb' Objectsresiduals.rglmb residuals.rlmb residuals.summary.rglmb
The Bayesian Generalized Linear Model Distributionprint.rglmb rglmb
The Bayesian Gaussian Regression with Independent Normal-Gamma Prior in Standard FormrIndepNormalGammaReg_std
The Bayesian Linear Model Distributionprint.rlmb rlmb
Bayesian Weighted Fitting Enginesglmb.wfit rNormal_reg.wfit
The Bayesian Generalized Linear Model Distribution in Standard FormrNormalGLM_std
Simulation Functions for Bayesian Generalized Linear Modelsprint.rGamma_reg print.simfunction rBeta_reg rGamma_Conjugate_reg rGamma_reg rindepNormalGamma_reg rNormalGamma_reg rNormal_reg simfuncs simfunction
Low-Level Simulation Pipeline for Bayesian GLMsSimulationPipeline
Summarizing Bayesian gamma_reg Distribution Functionsprint.summary.rGamma_reg summary.rGamma_reg
Summarizing Bayesian Generalized Linear Model Distribution Functionsprint.summary.rglmb summary.rglmb summary.rlmb
Sweeps required to reach a TV tolerancetwo_block_l_for_tv
Likelihood precision weights at the posterior modetwo_block_mode_weights
Two-block Gibbs sampler convergence rate (Remark 8 eigenvalues)two_block_rate
Convergence rate for the v2 (pfamily) two-block samplertwo_block_rate_v2
Two-block Gibbs sampler for hierarchical regressiontwo_block_rNormal_reg
Two-block Gibbs sampler with pfamily Block 2 priors (development v2)two_block_rNormal_reg_v2
Total-variation bound for the two-block Gibbs samplertwo_block_tv_bound