Package: ggdmc 0.2.8.9

ggdmc: Hierarchical Bayesian Inference for Choice Response Time Models

Provides tools for fitting hierarchical Bayesian models of choice and response time using Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampling. Designed for cognitive scientists and psychologists, it supports models including the Linear Ballistic Accumulator (LBA) and Diffusion Decision Model (DDM), offering flexible parameter mapping and condition-specific modelling. Implements fast, parallelised C++ routines for large-scale applications in decision modelling. Core functionality includes parameter sampling, simulation, model building, posterior recovery, and convergence diagnostics. Sampling methods follow Turner et al. (2013) <doi:10.1037/a0032222>, Braak (2006) <doi:10.1007/s11222-006-8769-1>, and Hu & Tsui (2010) <doi:10.1016/j.csda.2008.10.025>. The parameter mapping and condition-specific structure are based on Heathcote et al. (2018) <doi:10.3758/s13428-018-1067-y>.

Authors:Yi-Shin Lin [aut, cre]

ggdmc_0.2.8.9.tar.gz
ggdmc_0.2.8.9.zip(r-4.7)ggdmc_0.2.8.9.zip(r-4.6)ggdmc_0.2.8.9.zip(r-4.5)

ggdmc_0.2.8.9.tar.gz(r-4.7-arm64)ggdmc_0.2.8.9.tar.gz(r-4.7-x86_64)ggdmc_0.2.8.9.tar.gz(r-4.6-arm64)ggdmc_0.2.8.9.tar.gz(r-4.6-x86_64)
ggdmc_0.2.8.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ggdmc/json (API)
NEWS

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

Bug tracker:https://github.com/yxlin/ggdmc/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

4.76 score 19 stars 30 scripts 180 downloads 28 exports 9 dependencies

Last updated from:cee9795cce. Checks:7 NOTE, 2 OK, 4 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE148
linux-devel-x86_64NOTE109
source / vignettesOK216
linux-release-arm64NOTE133
linux-release-x86_64NOTE128
macos-release-arm64FAIL122
macos-release-x86_64FAIL193
macos-oldrel-arm64FAIL138
macos-oldrel-x86_64FAIL216
windows-develNOTE119
windows-releaseNOTE149
windows-oldrelNOTE129
wasm-releaseOK125

Exports:comparecompare_manygelmaninitialise_phiinitialise_thetaplotplot_thetasprepare_theta_dataprepare_thetas_dataprintRebuildHyperRebuildPosteriorRebuildPosteriorsRestartSamplingRestartSampling_hyperRestartSampling_subjectrunrun_hyperrun_subjectset_configsset_up_new_samplessetDEInputsetThetaInputStartSamplingStartSampling_hyperStartSampling_subjectsummarysummary_many

Dependencies:data.tableggdmcHeadersggdmcLikelihoodggdmcModelggdmcPriorlatticematrixStatsRcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Compare True Parameters to Estimated Quantilescompare
Compare Multiple Posterior Distributions Against True Valuescompare_many
MCMC Configuration Class and Constructorconfig-class set_configs
Differential Evolution (DE) MCMC Input Configurationde_input-class print,de_input-method setDEInput
Brook-Gelman Diagnostic (R-hat)gelman gelman,posterior-method gelman-methods
Hierarchical Bayesian Inference for Choice Response Time Modelsggdmc-package ggdmc
Initialize Hierarchical Parameters (Phi) for MCMC Samplinginitialise_phi
Initialise Theta Parameters for MCMC Samplinginitialise_theta
Plot Theta Parameters using Lattice Graphicsplot_thetas
Plot Posterior Distributions or Traces using Latticeplot,posterior-method
An S4 class to represent an object storing posterior samples.posterior-class
Construct Data Table from the Theta Estiamtesprepare_theta_data
Prepare Theta Parameters Data from MCMC Samplesprepare_thetas_data
Rebuild Posterior Objects from MCMC ChainsRebuildHyper RebuildPosterior RebuildPosteriors
Set Theta Storage for Modelset_up_new_samples
Create a theta_input Object for MCMC ConfigurationsetThetaInput
Initialise or Continue MCMC SamplingRestartSampling RestartSampling_hyper RestartSampling_subject StartSampling StartSampling_hyper StartSampling_subject
Summarise Multiple Posterior Objectssummary_many
Summarise Posterior Distributionsummary,posterior-method
An S4 class to store MCMC sampling parameters.theta_input-class
Print Method for theta_input Objectsprint,theta_input-method theta_input-print