Package: bayess 1.7

bayess: Bayesian Essentials with R

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

Authors:Jean-Michel Marin [aut, cre], Christian P. Robert [aut]

bayess_1.7.tar.gz
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bayess_1.7.tgz(r-4.6-any)bayess_1.7.tgz(r-4.5-any)
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bayess_1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bayess/json (API)

# Install 'bayess' in R:

install.packages('bayess', repos = c('https://jmm34.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jmm34/bayess/issues

Datasets:

bank - Bank dataset
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
Laichedata - Laiche dataset
Menteith - Grey-level image of the Lake of Menteith
normaldata - Normal dataset

On CRAN:

4.04 score 3 stars 73 scripts 493 downloads 42 exports 7 dependencies

Last updated from:bc2ac7833a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	159
source / vignettes	OK	231
linux-release	OK	111
macos-release	OK	139
macos-oldrel	OK	128
windows-devel	OK	75
windows-release	OK	83
windows-oldrel	OK	78
wasm-release	OK	98

Exports:ardipper ARllog ARmh BayesReg gibbs gibbscap1 gibbscap2 gibbsmean gibbsnorm hmflatlogit hmflatloglin hmflatprobit hmhmm hmmeantemp hmnoinflogit hmnoinfloglin hmnoinfprobit isinghm isingibbs logitll logitnoinflpost loglinll loglinnoinflpost MAllog MAmh ModChoBayesReg pbino pcapture pdarroch plotmix pottsgibbs pottshm probet probitll probitnoinflpost rdirichlet reconstruct solbeta sumising thresh truncnorm xneig4

Dependencies:bitops caTools combinat gplots gtools KernSmooth mnormt

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Accept-reject algorithm for the open population capture-recapture model	ardipper
log-likelihood associated with an AR(p) model defined either through its natural coefficients or through the roots of the associated lag-polynomial	ARllog
Metropolis-Hastings evaluation of the posterior associated with an AR(p) model	ARmh
bank dataset (Chapter 4)	bank
Bayesian linear regression output	BayesReg
Pine processionary caterpillar dataset	caterpillar
Non-standardised Licence dataset	datha
DNA sequence of an HIV genome	Dnadataset
European Dipper dataset	eurodip
Eurostoxx50 exerpt dataset	Eurostoxx50
Gibbs sampler and Chib's evidence approximation for a generic univariate mixture of normal distributions	gibbs
Gibbs sampler for the two-stage open population capture-recapture model	gibbscap1
Gibbs sampling for the Arnason-Schwarz capture-recapture model	gibbscap2
Gibbs sampler on a mixture posterior distribution with unknown means	gibbsmean
Gibbs sampler for a generic mixture posterior distribution	gibbsnorm
Metropolis-Hastings for the logit model under a flat prior	hmflatlogit
Metropolis-Hastings for the log-linear model under a flat prior	hmflatloglin
Metropolis-Hastings for the probit model under a flat prior	hmflatprobit
Estimation of a hidden Markov model with 2 hidden and 4 observed states	hmhmm likej
Metropolis-Hastings with tempering steps for the mean mixture posterior model	hmmeantemp
Metropolis-Hastings for the logit model under a noninformative prior	hmnoinflogit
Metropolis-Hastings for the log-linear model under a noninformative prior	hmnoinfloglin
Metropolis-Hastings for the probit model under a noninformative prior	hmnoinfprobit
Metropolis-Hastings for the Ising model	isinghm
Gibbs sampler for the Ising model	isingibbs
Laiche dataset	Laichedata
Log-likelihood of the logit model	logitll
Log of the posterior distribution for the probit model under a noninformative prior	logitnoinflpost
Log of the likelihood of the log-linear model	loglinll
Log of the posterior density for the log-linear model under a noninformative prior	loglinnoinflpost
log-likelihood associated with an MA(p) model	MAllog
Metropolis-Hastings evaluation of the posterior associated with an MA(p) model	MAmh
Grey-level image of the Lake of Menteith	Menteith
Bayesian model choice procedure for the linear model	ModChoBayesReg
Normal dataset	normaldata
Posterior expectation for the binomial capture-recapture model	pbino
Posterior probabilities for the multiple stage capture-recapture model	pcapture
Posterior probabilities for the Darroch model	pdarroch
Graphical representation of a normal mixture log-likelihood	plotmix
Gibbs sampler for the Potts model	pottsgibbs
Metropolis-Hastings sampler for a Potts model with 'ncol' classes	pottshm
Coverage of the interval (a,b) by the Beta cdf	probet
Log-likelihood of the probit model	probitll
Log of the posterior density for the probit model under a non-informative model	probitnoinflpost
Random generator for the Dirichlet distribution	rdirichlet
Image reconstruction for the Potts model with six classes	reconstruct
Recursive resolution of beta prior calibration	solbeta
Approximation by path sampling of the normalising constant for the Ising model	sumising
Bound for the accept-reject algorithm in Chapter 5	thresh
Random simulator for the truncated normal distribution	truncnorm
Number of neighbours with the same colour	xneig4