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bayess (version 1.4)

Bayesian Essentials with R

Description

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.

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Version

Install

install.packages('bayess')

Monthly Downloads

367

Version

1.4

License

GPL-2

Maintainer

Christian P Robert

Last Published

February 9th, 2013

Functions in bayess (1.4)

DNA sequence of an HIV genome

Metropolis--Hastings evaluation of the posterior associated with an MA(p) model

Accept-reject algorithm for the open population capture-recapture model

Gibbs sampler on a mixture posterior distribution with unknown means

Gibbs sampling for the Arnason-Schwarz capture-recapture model

Grey-level image of the Lake of Menteith

Metropolis-Hastings for the log-linear model under a noninformative prior

Gibbs sampler for a generic mixture posterior distribution

Posterior probabilities for the Darroch model

European Dipper dataset

Bayesian model choice procedure for the linear model

Gibbs sampler and Chib's evidence approximation for a generic univariate mixture of normal distributions

Gibbs sampler for the Ising model

Gibbs sampler for the two-stage open population capture-recapture model

Log-likelihood of the probit model

Random simulator for the truncated normal distribution

Non-standardised Licence dataset

Pine processionary caterpillar dataset

Coverage of the interval $(a,b)$ by the Beta cdf

bank dataset (Chapter 4)

Eurostoxx50 exerpt dataset

Random generator for the Dirichlet distribution

Bound for the accept-reject algorithm in Chapter 5

Bayesian linear regression output

Graphical representation of a normal mixture log-likelihood

Metropolis--Hastings evaluation of the posterior associated with an AR(p) model

log-likelihood associated with an MA(p) model

log-likelihood associated with an AR(p) model defined either through its natural coefficients or through the roots of the associated lag-polynomial

Posterior expectation for the binomial capture-recapture model

Estimation of a hidden Markov model with 2 hidden and 4 observed states

probitnoinflpost

Log of the posterior density for the probit model under a non-informative model

Approximation by path sampling of the normalising constant for the Ising model

Recursive resolution of beta prior calibration

Metropolis-Hastings sampler for a Potts model with ncol classes

Metropolis-Hastings for the probit model under a noninformative prior

Metropolis-Hastings for the probit model under a flat prior

Log of the likelihood of the log-linear model

loglinnoinflpost

Log of the posterior density for the log-linear model under a noninformative prior

Posterior probabilities for the multiple stage capture-recapture model

Gibbs sampler for the Potts model

Image reconstruction for the Potts model with six classes

Number of neighbours with the same colour

Metropolis-Hastings for the logit model under a flat prior

Metropolis-Hastings for the log-linear model under a flat prior

logitnoinflpost

Log of the posterior distribution for the probit model under a noninformative prior

Log-likelihood of the logit model

Metropolis-Hastings for the logit model under a noninformative prior

Metropolis-Hastings with tempering steps for the mean mixture posterior model

Metropolis-Hastings for the Ising model