A friendly MCMC framework
Append MCMC chains (objects of class coda::mcmc )
Checks the initial values of the MCMC
Convergence Monitoring
Recursive algorithms for computing variance and mean
Deprecated methods in fmcmc
A friendly MCMC framework
Adaptive Metropolis (AM) Transition Kernel
Mirror Transition Kernels
Transition Kernels for MCMC
Gaussian Transition Kernel
Robust Adaptive Metropolis (RAM) Transition Kernel
Uniform Transition Kernel
Functions to interact with the main loop
Information about the last MCMC call
Markov Chain Monte Carlo
Progress bar
Parameters' update sequence
Reflective Boundaries
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.