R: Bayesian Essentials with R

# Bayesian Essentials with R

## Documentation for package ‘bayess’ version 1.6

- DESCRIPTION file.
- Code demos. Use demo() to run them.

## Help Pages

ardipper | Accept-reject algorithm for the open population capture-recapture model
ARllog | log-likelihood associated with an AR(p) model defined either through its natural coefficients or through the roots of the associated lag-polynomial
ARmh | Metropolis-Hastings evaluation of the posterior associated with an AR(p) model
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 univariate mixture of normal distributions
gibbscap1 | Gibbs sampler for the two-stage open population capture-recapture model
gibbscap2 | Gibbs sampling for the Arnason-Schwarz capture-recapture model
gibbsmean | Gibbs sampler on a mixture posterior distribution with unknown means
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 prior
hmflatprobit | Metropolis-Hastings for the probit model under a flat prior
hmhmm | Estimation of a hidden Markov model with 2 hidden and 4 observed states
hmmeantemp | Metropolis-Hastings with tempering steps for the mean mixture posterior model
hmnoinflogit | Metropolis-Hastings for the logit model under a noninformative prior
hmnoinfloglin | Metropolis-Hastings for the log-linear model under a noninformative prior
hmnoinfprobit | Metropolis-Hastings for the probit model under a noninformative prior
isinghm | Metropolis-Hastings for the Ising model
isingibbs | Gibbs sampler for the Ising model
Laichedata | Laiche dataset
likej | Estimation of a hidden Markov model with 2 hidden and 4 observed states
logitll | Log-likelihood of the logit model
logitnoinflpost | Log of the posterior distribution for the probit model under a noninformative prior
loglinll | Log of the likelihood of the log-linear model
loglinnoinflpost | Log of the posterior density for the log-linear model under a noninformative prior
MAllog | log-likelihood associated with an MA(p) model
MAmh | Metropolis-Hastings evaluation of the posterior associated with an MA(p) model
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 model
pcapture | Posterior probabilities for the multiple stage capture-recapture model
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' classes
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 non-informative model
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 for the Ising model
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