run_many

a initialized samples list. Each element should contain
samples for a participant.

samples

report

ncore

turning parameter for crossover sampler

gammamult

ngroup

force

a string indicating to use which sampler

sampler

a Boolean switch to do slice sampling

slice

Use either DE-MCMC or DGMC sampler to fit independent Bayesian model to
many participants.

Hierarchical Bayesian modelling. The 'ggdmc' package provides tools designed for fitting cognitive models, using population-based Markov Chain Monte Carlo (pMCMC) and fast C++ libraries. The paper by Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2018, <doi:10.3758/s13428-018-1067-y>) describes the early prototype of the package (by the version 0.4.2). The paper accompanied with the revamped latest version (> 0.2.4.5) is under preparation Lin, Strickland, Reynold, and Heathcote (2018) and a further paper regarding the new pMCMC sampling modified based on Hu and Tsai's (2010) work is also under preparation. See 'citation("ggdmc")' for details.

run_many: Fit a Bayesian Model to multiple Participants

Description

Usage

Arguments