Cognitive Models
Bind data and models
Create a model object
Specifying Parameter Prior Distributions
Does a model object specify a correct p.vector
Prepare posterior samples for plotting functions version 1
A modified dbeta function
A modified dcauchy functions
A pseudo constant function to get constant densities
Calculate the statistics of model complexity
A modified dgamma function
Deviance information criteria
A modified dlnorm functions
Truncated Normal Distribution
Calculate effective sample sizes
Potential scale reduction factor
Retrieve information of operating system
Get a n-cell matrix
Constructs a ns x npar matrix,
Extract parameter names from a model object
Bayeisan computation of response time models
Model checking functions
Model checking functions
Model checking functions
Model checking functions
Calculate log likelihoods
Create a MCMC list
Which chains get stuck
Plot prior distributions
Print Prior Distribution
Generate random numbers
Generate Random Deviates of the LBA Distribution
Parameter Prior Distributions
Simulate response time data
Start new model fits
Summarise posterior samples
Summary statistic for posterior samples
Table response and parameter
Convert theta to a mcmc List
Unstick posterios samples (One subject)
Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.