Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.


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1.4 by Andreas Scheidegger, 8 months ago

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Authors: Andreas Scheidegger , <[email protected]> , <[email protected]>

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ramcmc

Depends on parallel, coda, Matrix

Imported by ConsReg, POUMM.

Depended on by EpiILM, selectiveInference.

Suggested by GUTS.

See at CRAN