Data Cloning and MCMC Tools for Maximum Likelihood Methods
Fit BUGS models with cloned data
Parallel computing with WinBUGS/OpenBUGS
Optimizing the number of workers
Size balancing
Generate posterior samples in mcmc.list format
Methods for the 'mcmc.list' class
Iterative model fitting with data cloning
Parallel model fitting with data cloning
Internal function for iterative model fitting with data cloning
Data Cloning
Cloning R objects
Manipulating dclone environments
Setting Options
Retrieve descriptive statistics from fitted objects to evaluate conver...
Plot error bars
Evaluates parallel argument
Fit JAGS models with cloned data
Parallel computing with JAGS
Create a JAGS model object
Data Cloning Diagnostics
Make a square matrix symmetric by averaging.
Size balancing version of mclapply
Calculations on 'mcmc.list' objects
Number of Clones
Scatterplot Matrices for 'mcmc.list' Objects
Parallel RNGs for initial values
Generate posterior samples in 'mcmc.list' format on parallel workers
Parallel wrapper function to call from within a function
Create a JAGS model object on parallel workers
Dynamically load JAGS modules on parallel workers
Advanced control over JAGS on parallel workers
Update jags models on parallel workers
Fit Stan models with cloned data
Automatic updating of an MCMC object from JAGS
Write and remove model file
Low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods as described in Solymos 2010 <doi:10.32614/RJ-2010-011>. Sequential and parallel MCMC support for 'JAGS', 'WinBUGS', 'OpenBUGS', and 'Stan'.
Useful links