Package: ggdmc 0.2.6.0

Yi-Shin Lin

ggdmc: Cognitive Models

Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.

Authors:Yi-Shin Lin [aut, cre], Andrew Heathcote [aut]

ggdmc_0.2.6.0.tar.gz
ggdmc_0.2.6.0.tar.gz(r-4.5-noble)ggdmc_0.2.6.0.tar.gz(r-4.4-noble)
ggdmc_0.2.6.0.tgz(r-4.4-emscripten)ggdmc_0.2.6.0.tgz(r-4.3-emscripten)
ggdmc.pdf |ggdmc.html✨
ggdmc/json (API)

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

Peer review:

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

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

2.08 score 1 stars 24 scripts 305 downloads 43 exports 33 dependencies

Last updated 6 years agofrom:1ce5eefc37. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-linux-x86_64NOTENov 21 2024

Exports:BPICBuildDMIBuildModelBuildPriorcheck_pvecConvertChainsdbeta_ludcauchy_ldconstantdgamma_lDICdlnorm_ldtnormeffectiveSizeeffectiveSize_hypereffectiveSize_manyeffectiveSize_onegelmanget_osGetNsimGetParameterMatrixGetPNameshgelmaniseffectiveisflatismixedisstucklikelihoodmcmc_list.modelphi2mcmclistPickStuckplot_priorptnormrandomrlba_normrpriorrtnormrunStartNewsamplessummary_mcmc_listTableParameterstheta2mcmclistunstick_one

Dependencies:clicodacolorspacedata.tablefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Bind data and modelsBuildDMI
Create a model objectBuildModel print.dmi print.model
Specifying Parameter Prior DistributionsBuildPrior
Does a model object specify a correct p.vectorcheck_pvec
Prepare posterior samples for plotting functions version 1ConvertChains
A modified dbeta functiondbeta_lu
A modified dcauchy functionsdcauchy_l
A pseudo constant function to get constant densitiesdconstant
Calculate the statistics of model complexitydeviance.model
A modified dgamma functiondgamma_l
Deviance information criteriaBPIC DIC
A modified dlnorm functionsdlnorm_l
Truncated Normal Distributiondtnorm ptnorm rtnorm
Calculate effective sample sizeseffectiveSize effectiveSize_hyper effectiveSize_many effectiveSize_one
Potential scale reduction factorgelman hgelman
Retrieve information of operating systemget_os
Get a n-cell matrixGetNsim
Constructs a ns x npar matrix,GetParameterMatrix
Extract parameter names from a model objectGetPNames
Bayeisan computation of response time modelsggdmc-package ggdmc
Model checking functionsiseffective
Model checking functionsisflat
Model checking functionsismixed
Model checking functionsCheckConverged isstuck
Calculate log likelihoodslikelihood
Create a MCMC listmcmc_list.model
Which chains get stuckPickStuck
Plot prior distributionsplot.prior plot_prior
Print Prior Distributionprint.prior
Generate random numbersrandom
Generate Random Deviates of the LBA Distributionrlba_norm
Parameter Prior Distributionsrprior
Simulate response time datasimulate.model
Start new model fitsrun StartNewsamples
Summary statistic for posterior samplessummary_mcmc_list
Summarise posterior samplessummary.model
Table response and parameterTableParameters
Convert theta to a mcmc Listphi2mcmclist theta2mcmclist
Unstick posterios samples (One subject)unstick_one