Cognitive Models

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Documentation for package ‘ggdmc’ version 0.2.6.0

• DESCRIPTION file.

Help Pages

ggdmc-package	Bayeisan computation of response time models
BPIC	Deviance information criteria
BuildDMI	Bind data and models
BuildModel	Create a model object
BuildPrior	Specifying Parameter Prior Distributions
CheckConverged	Model checking functions
check_pvec	Does a model object specify a correct p.vector
ConvertChains	Prepare posterior samples for plotting functions version 1
dbeta_lu	A modified dbeta function
dcauchy_l	A modified dcauchy functions
dconstant	A pseudo constant function to get constant densities
deviance.model	Calculate the statistics of model complexity
dgamma_l	A modified dgamma function
DIC	Deviance information criteria
dlnorm_l	A modified dlnorm functions
dtnorm	Truncated Normal Distribution
effectiveSize	Calculate effective sample sizes
effectiveSize_hyper	Calculate effective sample sizes
effectiveSize_many	Calculate effective sample sizes
effectiveSize_one	Calculate effective sample sizes
gelman	Potential scale reduction factor
GetNsim	Get a n-cell matrix
GetParameterMatrix	Constructs a ns x npar matrix,
GetPNames	Extract parameter names from a model object
get_os	Retrieve information of operating system
ggdmc	Bayeisan computation of response time models
hgelman	Potential scale reduction factor
iseffective	Model checking functions
isflat	Model checking functions
ismixed	Model checking functions
isstuck	Model checking functions
likelihood	Calculate log likelihoods
mcmc_list.model	Create a MCMC list
phi2mcmclist	Convert theta to a mcmc List
PickStuck	Which chains get stuck
plot.prior	Plot prior distributions
plot_prior	Plot prior distributions
print.dmi	Create a model object
print.model	Create a model object
print.prior	Print Prior Distribution
ptnorm	Truncated Normal Distribution
random	Generate random numbers
rlba_norm	Generate Random Deviates of the LBA Distribution
rprior	Parameter Prior Distributions
rtnorm	Truncated Normal Distribution
run	Start new model fits
simulate.model	Simulate response time data
StartNewsamples	Start new model fits
summary.model	Summarise posterior samples
summary_mcmc_list	Summary statistic for posterior samples
TableParameters	Table response and parameter
theta2mcmclist	Convert theta to a mcmc List
unstick_one	Unstick posterios samples (One subject)