All functions
applySettingsDefault	Provides the default settings for the different samplers in runMCMC
BayesianTools	BayesianTools
bridgesample	Calculates the marginal likelihood of a chain via bridge sampling
checkBayesianSetup	Checks if an object is of class 'BayesianSetup'
convertCoda	Convert coda::mcmc objects to BayesianTools::mcmcSampler
correctThin	Checks if thin is conistent with nTotalSamples samples and if not corrects it.
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
createBreakMat	create break matrix
createLikelihood	Creates a standardized likelihood class#'
createMcmcSamplerList	Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
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	Runs Gelman Diagnotics over an BayesianOutput
generateParallelExecuter	Factory to generate a parallel executer of an existing function
generateTestDensityMultiNormal	Multivariate normal likelihood
getCredibleIntervals	Calculate confidence region from an MCMC or similar sample
getDharmaResiduals	Creates a DHARMa object
getMetropolisDefaultSettings	Returns Metropolis default settings
getPanels	Calculates the panel combination for par(mfrow = )
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
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
histMarginal	histogram for marginalPlot
likelihoodAR1	AR1 type likelihood function
likelihoodIidNormal	Normal / Gaussian Likelihood function
logSumExp	Funktion to compute log(sum(exp(x))
MAP	calculates the Maxiumum APosteriori value (MAP)
marginalLikelihood	Calcluated the marginal likelihood from a set of MCMC samples
marginalPlot	Plot MCMC marginals
mcmcMultipleChains	Run multiple chains
Metropolis	Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering
metropolisRatio	Function to calculate the metropolis ratio
createMixWithDefaults	Allows to mix a given parameter vector with a default parameter vector
plotHist	plot histogram
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
rescale	Rescale
runMCMC	Main wrapper function to start MCMCs, particle MCMCs and SMCs
sampleEquallySpaced	Gets n equally spaced samples (rows) from a matrix or vector
sampleMetropolis	gets samples while adopting the MCMC proposal generator
setupStartProposal	Help function to find starvalues and proposalGenerator settings
smcSampler	SMC sampler
stopParallel	Function to close cluster in BayesianSetup
testDensityBanana	Banana-shaped density 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
violinPlot	Violin Plot
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