This is the parent R6 class of the Auto-correlated Bayesian Sampler (ABS, Zhu et al. 2024) . It is a sequential sampling model assuming people draw autocorrelated samples from memory or beliefs, i.e., posterior of hypotheses.
Zhu J, Sundh J, Spicer J, Chater N, Sanborn AN (2024). “The Autocorrelated Bayesian Sampler: A Rational Process for Probability Judgments, Estimates, Confidence Intervals, Choices, Confidence Judgments, and Response Times.” Psychological Review, 131(2), 456–493. doi:10.1037/rev0000427 .
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.