General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

---

Documentation for package ‘BayesianTools’ version 0.1.8

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

applySettingsDefault	Provides the default settings for the different samplers in runMCMC
BayesianTools	BayesianTools
calibrationTest	Simulation-based calibration tests
checkBayesianSetup	Checks if an object is of class 'BayesianSetup'
convertCoda	Convert coda::mcmc objects to BayesianTools::mcmcSampler
correlationPlot	Flexible function to create correlation density plots
createBayesianSetup	Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.
createBetaPrior	Convenience function to create a beta prior
createLikelihood	Creates a standardized likelihood class#'
createMcmcSamplerList	Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
createMixWithDefaults	Allows to mix a given parameter vector with a default parameter vector
createPosterior	Creates a standardized posterior class
createPrior	Creates a standardized prior class
createPriorDensity	Fits a density function to a multivariate sample
createProposalGenerator	Factory that creates a proposal generator
createSmcSamplerList	Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers
createTruncatedNormalPrior	Convenience function to create a truncated normal prior
createUniformPrior	Convenience function to create a simple uniform prior distribution
DE	Differential-Evolution MCMC
DEzs	Differential-Evolution MCMC zs
DIC	Deviance information criterion
DREAM	DREAM
DREAMzs	DREAMzs
gelmanDiagnostics	Gelman Diagnostics
generateParallelExecuter	Factory to generate a parallel executor of an existing function
generateTestDensityMultiNormal	Multivariate normal likelihood
getCredibleIntervals	Calculate confidence region from an MCMC or similar sample
getDharmaResiduals	Creates a DHARMa object
getPanels	getPanels
getPossibleSamplerTypes	Returns possible sampler types
getPredictiveDistribution	Calculates predictive distribution based on the parameters
getPredictiveIntervals	Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample
getSample	Extracts the sample from a bayesianOutput
getSample.data.frame	Extracts the sample from a bayesianOutput
getSample.double	Extracts the sample from a bayesianOutput
getSample.integer	Extracts the sample from a bayesianOutput
getSample.list	Extracts the sample from a bayesianOutput
getSample.matrix	Extracts the sample from a bayesianOutput
getSample.MCMC	Extracts the sample from a bayesianOutput
getSample.mcmc	Extracts the sample from a bayesianOutput
getSample.mcmc.list	Extracts the sample from a bayesianOutput
getSample.MCMC_refClass	Extracts the sample from a bayesianOutput
getVolume	Calculate posterior volume
GOF	Standard GOF metrics Startvalues for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide a list
likelihoodAR1	AR1 type likelihood function
likelihoodIidNormal	Normal / Gaussian Likelihood function
MAP	calculates the Maxiumum APosteriori value (MAP)
marginalLikelihood	Calcluated the marginal likelihood from a set of MCMC samples
marginalPlot	Plot MCMC marginals
mergeChains	Merge Chains
Metropolis	Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering
plotDiagnostic	Diagnostic Plot
plotSensitivity	Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range.
plotTimeSeries	Plots a time series, with the option to include confidence and prediction band
plotTimeSeriesResiduals	Plots residuals of a time series
plotTimeSeriesResults	Creates a time series plot typical for an MCMC / SMC fit
runMCMC	Main wrapper function to start MCMCs, particle MCMCs and SMCs
smcSampler	SMC sampler
stopParallel	Function to close cluster in BayesianSetup
testDensityBanana	Banana-shaped density function
testDensityGelmanMeng	GelmanMeng test function
testDensityInfinity	Test function infinity ragged
testDensityMultiNormal	3d Mutivariate Normal likelihood
testDensityNormal	Normal likelihood
testLinearModel	Fake model, returns a ax + b linear response to 2-param vector
tracePlot	Trace plot for MCMC class
Twalk	T-walk MCMC
updateProposalGenerator	To update settings of an existing proposal genenerator
VSEM	Very simple ecosystem model
vsemC	C version of the VSEM model
VSEMcreateLikelihood	Create an example dataset, and from that a likelihood or posterior for the VSEM model
VSEMcreatePAR	Create a random radiation (PAR) time series
VSEMgetDefaults	returns the default values for the VSEM
WAIC	calculates the WAIC