Bayesian Essentials with R

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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