Man pages for ggdmc
Cognitive Models

BuildDMIBind data and models
BuildModelCreate a model object
BuildPriorSpecifying Parameter Prior Distributions
check_pvecDoes a model object specify a correct p.vector
ConvertChainsPrepare posterior samples for plotting functions version 1
dbeta_luA modified dbeta function
dcauchy_lA modified dcauchy functions
dconstantA pseudo constant function to get constant densities
deviance.modelCalculate the statistics of model complexity
dgamma_lA modified dgamma function
DICDeviance information criteria
dlnorm_lA modified dlnorm functions
dtnormTruncated Normal Distribution
effectiveSizeCalculate effective sample sizes
gelmanPotential scale reduction factor
GetNsimGet a n-cell matrix
get_osRetrieve information of operating system
GetParameterMatrixConstructs a ns x npar matrix,
GetPNamesExtract parameter names from a model object
ggdmcBayeisan computation of response time models
iseffectiveModel checking functions
isflatModel checking functions
ismixedModel checking functions
isstuckModel checking functions
likelihoodCalculate log likelihoods
mcmc_list.modelCreate a MCMC list
PickStuckWhich chains get stuck
plot_priorPlot prior distributions
print.priorPrint Prior Distribution
randomGenerate random numbers
rlba_normGenerate Random Deviates of the LBA Distribution
rpriorParameter Prior Distributions
simulate.modelSimulate response time data
StartNewsamplesStart new model fits
summary_mcmc_listSummary statistic for posterior samples
summary.modelSummarise posterior samples
TableParametersTable response and parameter
theta2mcmclistConvert theta to a mcmc List
unstick_oneUnstick posterios samples (One subject)
ggdmc documentation built on May 2, 2019, 9:59 a.m.