ggdmc: Bayeisan computation of response time models
In ggdmc: Cognitive Models
Description
Author(s)
References
Description
ggdmc uses the population-based Markov chain Monte Carlo to
conduct Bayesian computation on cognitive models.
Author(s)
Yi-Shin Lin <yishinlin001@gmail.com>
Andrew Heathcote <andrew.heathcote@utas.edu.au>
References
Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M. &
Matzke, D., (2018). Dynamic model of choice.
Behavior Research Methods.
https://doi.org/10.3758/s13428-018-1067-y.
Turner, B. M., & Sederberg P. B. (2012). Approximate Bayesian computation
with differential evolution, Journal of Mathematical Psychology, 56,
375–385.
Ter Braak (2006). A Markov Chain Monte Carlo version of the genetic
algorithm Differential Evolution: easy Bayesian computing for real
parameter spaces. Statistics and Computing, 16, 239-249.
ggdmc documentation built on May 2, 2019, 9:59 a.m.
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Description Author(s) References
Description
ggdmc uses the population-based Markov chain Monte Carlo to conduct Bayesian computation on cognitive models.
Author(s)
Yi-Shin Lin <yishinlin001@gmail.com>
Andrew Heathcote <andrew.heathcote@utas.edu.au>
References
Heathcote, A., Lin, Y.-S., Reynolds, A., Strickland, L., Gretton, M. &
Matzke, D., (2018). Dynamic model of choice.
Behavior Research Methods.
https://doi.org/10.3758/s13428-018-1067-y.
Turner, B. M., & Sederberg P. B. (2012). Approximate Bayesian computation
with differential evolution, Journal of Mathematical Psychology, 56,
375–385.
Ter Braak (2006). A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239-249.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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