bayess contains a collection of functions that allows the reenactment of the R programs used in the book "Bayesian Essentials with R" (revision of "Bayesian Core") without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations.

Author | Christian P. Robert, Universite Paris Dauphine, and Jean-Michel Marin, Universite Montpellier 2 |

Date of publication | 2013-02-09 22:07:40 |

Maintainer | Christian P. Robert <xian@ceremade.dauphine.fr> |

License | GPL-2 |

Version | 1.4 |

**ardipper:** Accept-reject algorithm for the open population...

**ARllog:** log-likelihood associated with an AR(p) model defined either...

**ARmh:** Metropolis-Hastings evaluation of the posterior associated...

**bank:** bank dataset (Chapter 4)

**BayesReg:** Bayesian linear regression output

**caterpillar:** Pine processionary caterpillar dataset

**datha:** Non-standardised Licence dataset

**Dnadataset:** DNA sequence of an HIV genome

**eurodip:** European Dipper dataset

**Eurostoxx50:** Eurostoxx50 exerpt dataset

**gibbs:** Gibbs sampler and Chib's evidence approximation for a generic...

**gibbs2:** Gibbs sampler for the two-stage open population...

**gibbs3:** Gibbs sampling for the Arnason-Schwarz capture-recapture...

**gibbsmean:** Gibbs sampler on a mixture posterior distribution with...

**gibbsnorm:** Gibbs sampler for a generic mixture posterior distribution

**hmflatlogit:** Metropolis-Hastings for the logit model under a flat prior

**hmflatloglin:** Metropolis-Hastings for the log-linear model under a flat...

**hmflatprobit:** Metropolis-Hastings for the probit model under a flat prior

**hmhmm:** Estimation of a hidden Markov model with 2 hidden and 4...

**hmmeantemp:** Metropolis-Hastings with tempering steps for the mean mixture...

**hmnoinflogit:** Metropolis-Hastings for the logit model under a...

**hmnoinfloglin:** Metropolis-Hastings for the log-linear model under a...

**hmnoinfprobit:** Metropolis-Hastings for the probit model under a...

**isinghm:** Metropolis-Hastings for the Ising model

**isingibbs:** Gibbs sampler for the Ising model

**Laiche:** Laiche dataset

**logitll:** Log-likelihood of the logit model

**logitnoinflpost:** Log of the posterior distribution for the probit model under...

**loglinll:** Log of the likelihood of the log-linear model

**loglinnoinflpost:** Log of the posterior density for the log-linear model under a...

**MAllog:** log-likelihood associated with an MA(p) model

**MAmh:** Metropolis-Hastings evaluation of the posterior associated...

**Menteith:** Grey-level image of the Lake of Menteith

**ModChoBayesReg:** Bayesian model choice procedure for the linear model

**normaldata:** Normal dataset

**pbino:** Posterior expectation for the binomial capture-recapture...

**pcapture:** Posterior probabilities for the multiple stage...

**pdarroch:** Posterior probabilities for the Darroch model

**plotmix:** Graphical representation of a normal mixture log-likelihood

**pottsgibbs:** Gibbs sampler for the Potts model

**pottshm:** Metropolis-Hastings sampler for a Potts model with 'ncol'...

**probet:** Coverage of the interval (a,b) by the Beta cdf

**probitll:** Log-likelihood of the probit model

**probitnoinflpost:** Log of the posterior density for the probit model under a...

**rdirichlet:** Random generator for the Dirichlet distribution

**reconstruct:** Image reconstruction for the Potts model with six classes

**solbeta:** Recursive resolution of beta prior calibration

**sumising:** Approximation by path sampling of the normalising constant...

**thresh:** Bound for the accept-reject algorithm in Chapter 5

**truncnorm:** Random simulator for the truncated normal distribution

**xneig4:** Number of neighbours with the same colour

bayess

bayess/MD5

bayess/demo

bayess/demo/Chapter.7.R

bayess/demo/Chapter.4.R

bayess/demo/Chapter.3.R

bayess/demo/Chapter.2.R

bayess/demo/Chapter.6.R

bayess/demo/Chapter.8.R

bayess/demo/Chapter.1.R

bayess/demo/00Index

bayess/demo/Chapter.5.R

bayess/NAMESPACE

bayess/data

bayess/data/bank.txt.gz

bayess/data/Eurostoxx50.txt.gz

bayess/data/normaldata.txt.gz

bayess/data/datha.txt.gz

bayess/data/Menteith.txt.gz

bayess/data/eurodip.txt.gz

bayess/data/Laichedata.txt.gz

bayess/data/Dnadataset.txt.gz

bayess/data/caterpillar.txt.gz

bayess/DESCRIPTION

bayess/man

bayess/man/hmflatlogit.Rd
bayess/man/hmnoinfprobit.Rd
bayess/man/logitnoinflpost.Rd
bayess/man/hmflatprobit.Rd
bayess/man/gibbs2.Rd
bayess/man/reconstruct.Rd
bayess/man/rdirichlet.Rd
bayess/man/sumising.Rd
bayess/man/Dnadataset.Rd
bayess/man/caterpillar.Rd
bayess/man/probitnoinflpost.Rd
bayess/man/pottsgibbs.Rd
bayess/man/hmnoinflogit.Rd
bayess/man/bank.Rd
bayess/man/thresh.Rd
bayess/man/isinghm.Rd
bayess/man/probet.Rd
bayess/man/hmmeantemp.Rd
bayess/man/gibbsnorm.Rd
bayess/man/plotmix.Rd
bayess/man/Eurostoxx50.Rd
bayess/man/Laiche.Rd
bayess/man/normaldata.Rd
bayess/man/isingibbs.Rd
bayess/man/solbeta.Rd
bayess/man/datha.Rd
bayess/man/loglinnoinflpost.Rd
bayess/man/truncnorm.Rd
bayess/man/probitll.Rd
bayess/man/pbino.Rd
bayess/man/hmhmm.Rd
bayess/man/hmnoinfloglin.Rd
bayess/man/gibbs.Rd
bayess/man/ModChoBayesReg.Rd
bayess/man/pottshm.Rd
bayess/man/ardipper.Rd
bayess/man/ARllog.Rd
bayess/man/loglinll.Rd
bayess/man/gibbs3.Rd
bayess/man/hmflatloglin.Rd
bayess/man/logitll.Rd
bayess/man/eurodip.Rd
bayess/man/pdarroch.Rd
bayess/man/pcapture.Rd
bayess/man/MAllog.Rd
bayess/man/Menteith.Rd
bayess/man/xneig4.Rd
bayess/man/BayesReg.Rd
bayess/man/gibbsmean.Rd
bayess/man/ARmh.Rd
bayess/man/MAmh.Rd
bayess/R

bayess/R/sumising.R
bayess/R/pbino.R
bayess/R/ARllog.R
bayess/R/loglinnoinflpost.R
bayess/R/BayesReg.R
bayess/R/pottsgibbs.R
bayess/R/truncnorm.R
bayess/R/hmnoinfloglin.R
bayess/R/gibbsmean.R
bayess/R/logitnoinflpost.R
bayess/R/MAmh.R
bayess/R/ModChoBayesReg.R
bayess/R/gibbscap1.R
bayess/R/probitll.R
bayess/R/thresh.R
bayess/R/logitll.R
bayess/R/gibbs.R
bayess/R/hmflatprobit.R
bayess/R/gibbsnorm.R
bayess/R/probitnoinflpost.R
bayess/R/MAllog.R
bayess/R/hmhmm.R
bayess/R/rdirichlet.R
bayess/R/pcapture.R
bayess/R/hmflatloglin.R
bayess/R/reconstruct.R
bayess/R/plotmix.R
bayess/R/ARmh.R
bayess/R/isinghm.R
bayess/R/solbeta.R
bayess/R/hmnoinfprobit.R
bayess/R/pottshm.R
bayess/R/ardipper.R
bayess/R/probet.R
bayess/R/pdarroch.R
bayess/R/hmmeantemp.R
bayess/R/hmflatlogit.R
bayess/R/hmnoinflogit.R
bayess/R/gibbscap2.R
bayess/R/xneig4.R
bayess/R/isingibbs.R
bayess/R/loglinll.R
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