This is the parent R6 class of the Auto-correlated Bayesian Sampler @ABS, @zhu2024AutocorrelatedBayesianSamplersamplr. It is a sequential sampling model assuming people draw autocorrelated samples from memory or beliefs, i.e., posterior of hypotheses.
n_chainsan integer of the number of chains for the sampler.
nd_timea numeric value of the non-decision time (in seconds).
s_nd_timea numeric value of the inter-trial-variability of the non-decision time (in seconds).
distr_namea character string indicating the type of the posterior hypothesis distribution.
distr_paramsa numeric vector of the additional parameters for the posterior hypothesis distribution.
custom_distra list of functions that define the posterior hypothesis distribution.
custom_starta numeric value of the starting point if "custom_distr" is provided.
sim_resultsa data frame for saving the simulation results.
new()Create a new 'CoreABS' object.
CoreABS$new(
n_chains,
nd_time,
s_nd_time,
distr_name = NULL,
distr_params = NULL,
custom_distr = NULL,
custom_start = NULL
)n_chainsan integer of the number of chains for the sampler.
nd_timea numeric value of the non-decision time (in seconds).
s_nd_timea numeric value of the inter-trial-variability of the non-decision time (in seconds).
distr_namea character string indicating the type of the posterior hypothesis distribution. The package currently only supports norm, which represents normal distribution.
distr_paramsa numeric vector of the additional parameters for the posterior hypothesis distribution.
custom_distra list of functions that define the posterior hypothesis distribution.
custom_starta numeric value of the starting point if "custom_distr" is provided.
A new 'CoreABS' object.
clone()The objects of this class are cloneable with this method.
CoreABS$clone(deep = FALSE)deepWhether to make a deep clone.