fmcmc: A friendly MCMC framework. The fmcmc R package provides a lightweight general framework for implementing Markov Chain Monte Carlo methods based on the Metropolis-Hasing algorithm. This implementation’s main purpose lies in the fact that the user can incorporate the following in a flexible way: Automatic convergence checker: The algorithm splits the MCMC runs according to the frequency with which it needs to check convergence. Users can use either one of the included functions (convergence_gelman, convergence_geweke, etc.), or provide their own. Run multiple chains in parallel fashion: Using either a PSOCK cluster (default), or providing a personalized cluster object like the ones in the parallel R package. User defined transition kernels: Besides of canonical Gaussian Kernel, users can specify their own or use one of the included in the package, for example: kernel_normal, kernel_unif, or kernel_reflective. All the above without requiring compiled code.

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References in zbMATH (referenced in 1 article , 1 standard article )

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  1. George G Vega Yon; Paul Marjoram: fmcmc: A friendly MCMC framework (2019) not zbMATH