Description: 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.

Title: MCMC Generalised Linear Mixed Models
Depends:  MASS, corpcor, tensorA, Matrix, coda, ape, rgl, pscl, orthopolynom, combinat, gtools
Description: MCMC Generalised Linear Mixed Models
Packaged: Mon Jan 26 21:01:03 2009; jarrod

Packaged: Sun Feb  1 12:42:58 2009; root

Packaged: Sat Feb  7 12:27:12 2009; jarrod

Depends:  MASS, corpcor, tensorA, Matrix, coda, ape, pscl, orthopolynom, combinat, gtools
Packaged: Tue Feb 24 16:41:33 2009; jarrod

Packaged: Tue Mar  3 13:17:16 2009; jarrod

Packaged: Fri Mar  6 15:11:06 2009; jarrod

Depends: MASS, corpcor, tensorA, Matrix, coda, ape, pscl, orthopolynom,
Packaged: Tue Mar 17 12:22:22 2009; jarrod

Packaged: Sat Apr 4 18:05:22 2009; jarrod

Packaged: Tue Apr 14 16:53:46 2009; jarrod

Packaged: Fri Apr 17 16:24:25 2009; jarrod

Packaged: Thu May 14 20:52:48 2009; jarrod

Depends: tensorA, Matrix, coda, ape, corpcor
Suggests: rgl, combinat, mvtnorm, orthopolynom

Imports: corpcor, tensorA, cubature, methods
Description: MCMC Generalised Linear Mixed Models.

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

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

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

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

Package: MCMChybridGP
Type: Package
Title: Hybrid Markov chain Monte Carlo using Gaussian Processes
Version: 2.1
Date: 2009-09-22
Author: Mark J. Fielding <stafmj@nus.edu.sg>
Maintainer: Mark J. Fielding <stafmj@nus.edu.sg>
Depends: MASS
Description: 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).
License: GPL-2
LazyLoad: yes
Packaged: 2009-09-22 06:14:26 UTC; mark
Repository: CRAN
Date/Publication: 2009-09-22 08:22:06