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.

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

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