pmwg-package: pmwg: Particle Metropolis Within Gibbs.
In pmwg: Particle Metropolis Within Gibbs
Description
Documentation
User input
Author(s)
References
See Also
Description
The pmwg package provides a general purpose implementation of the
sampling techniques outlined in Gunawan et al. (2020)
doi: 10.1016/j.jmp.2020.102368. The user of this package is required to
provide their own log likelihood function, but given this the functions
provided can estimate model parameters, the full covariance matrix and
subject random effects in a hierarchical Bayesian way.
Documentation
The documentation found at https://newcastlecl.github.io/samplerDoc/
contains background information and motivation for the approach used in
this package and several detailed examples of the package in action. It also
includes a list of common problems and associated troubleshooting steps.
User input
The user is expected to provide a data source in a format that is compatible
with R data.frame methods. This data must have at least one column named
‘subject' that has a unique identifier for each subject’s data.
Additionally the user should provide a function that when given a set of
parameter estimates and the data for a single subject return the log of the
likelihood of that data given the parameter estimates.
The final piece of required information is a list of the names of each
parameter that should be estimated. There is also the capability to provide
priors on the model parameters, start points for the model parameters and
covariance matrix as well as options to fine tune the sampling process
Author(s)
Maintainer: Gavin Cooper gavin@gavincooper.net (Package creator and maintainer) [translator]
Authors:
Reilly Innes Reilly.Innes@uon.edu.au
Caroline Kuhne caroline.kuhne@newcastle.edu.au
Jon-Paul Cavallaro jon-paul.cavallaro@uon.edu.au
David Gunawan dgunawan@uow.edu.au (Author of original MATLAB code)
Guy Hawkins guy.hawkins@newcastle.edu.au
Scott Brown scott.brown@newcastle.edu.au (Original translation from MATLAB to R) [translator]
References
Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020).
New estimation approaches for the hierarchical Linear Ballistic Accumulator
model. Journal of Mathematical Psychology, 96, 102368.
See Also
Useful links:
pmwg documentation built on Feb. 17, 2021, 9:07 a.m.
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Description Documentation User input Author(s) References See Also
Description
The pmwg package provides a general purpose implementation of the sampling techniques outlined in Gunawan et al. (2020) doi: 10.1016/j.jmp.2020.102368. The user of this package is required to provide their own log likelihood function, but given this the functions provided can estimate model parameters, the full covariance matrix and subject random effects in a hierarchical Bayesian way.
Documentation
The documentation found at https://newcastlecl.github.io/samplerDoc/ contains background information and motivation for the approach used in this package and several detailed examples of the package in action. It also includes a list of common problems and associated troubleshooting steps.
User input
The user is expected to provide a data source in a format that is compatible with R data.frame methods. This data must have at least one column named ‘subject' that has a unique identifier for each subject’s data.
Additionally the user should provide a function that when given a set of parameter estimates and the data for a single subject return the log of the likelihood of that data given the parameter estimates.
The final piece of required information is a list of the names of each parameter that should be estimated. There is also the capability to provide priors on the model parameters, start points for the model parameters and covariance matrix as well as options to fine tune the sampling process
Author(s)
Maintainer: Gavin Cooper gavin@gavincooper.net (Package creator and maintainer) [translator]
Authors:
Reilly Innes Reilly.Innes@uon.edu.au
Caroline Kuhne caroline.kuhne@newcastle.edu.au
Jon-Paul Cavallaro jon-paul.cavallaro@uon.edu.au
David Gunawan dgunawan@uow.edu.au (Author of original MATLAB code)
Guy Hawkins guy.hawkins@newcastle.edu.au
Scott Brown scott.brown@newcastle.edu.au (Original translation from MATLAB to R) [translator]
References
Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020). New estimation approaches for the hierarchical Linear Ballistic Accumulator model. Journal of Mathematical Psychology, 96, 102368.
See Also
Useful links:
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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