Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.

MASS, corpcor, tensorA, Matrix, coda, ape, rgl, pscl, orthopolynom, combinat, gtools

MASS, corpcor, tensorA, Matrix, coda, ape, pscl, orthopolynom, combinat, gtools

MASS, corpcor, tensorA, Matrix, coda, ape, pscl, orthopolynom,
combinat, gtools

Fits Multi-response Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques.

Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques.

Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).

rgl, combinat, mvtnorm, orthopolynom, MCMCpack, bayesm, msm

Hybrid Markov chain Monte Carlo using Gaussian Processes
Hybrid Markov chain Monte Carlo (MCMC) to simulate from a
multimodal target distribution.  A Gaussian process
approximation makes this possible when derivatives are unknown.
The Package serves to minimize the number of function
evaluations in Bayesian calibration of computer models using
parallel tempering.  It allows replacement of the true target
distribution in high temperature chains, or complete
replacement of the target.  Methods used are described in,
"Efficient MCMC schemes for Bayesian calibration of computer
models", Fielding, Mark, Nott, David J. and Liong Shie-Yui
(2009), in preparation. The authors gratefully acknowledge the
support & contributions of the Singapore-Delft Water Alliance
(SDWA).  The research presented in this work was carried out as
part of the SDWA's Multi-Objective Multi-Reservoir Management
research programme (R-264-001-272).