bayess: Bayesian Essentials with R

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.

AuthorChristian P. Robert, Universite Paris Dauphine, and Jean-Michel Marin, Universite Montpellier 2
Date of publication2013-02-09 22:07:40
MaintainerChristian P. Robert <xian@ceremade.dauphine.fr>
LicenseGPL-2
Version1.4

View on CRAN

Man pages

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

Files in this package

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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