BayesianTools           BayesianTools
DE                      Differential-Evolution MCMC
DEzs                    Differential-Evolution MCMC zs
DIC                     Deviance information criterion
DREAM                   DREAM
DREAMzs                 DREAMzs
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
MAP                     calculates the Maxiumum APosteriori value (MAP)
Metropolis              Creates a Metropolis-type MCMC with options for
                        covariance adaptatin, delayed rejection,
                        Metropolis-within-Gibbs, and tempering
Twalk                   T-walk MCMC
VSEM                    Very simple ecosystem 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
applySettingsDefault    Provides the default settings for the different
                        samplers in runMCMC
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
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
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
likelihoodAR1           AR1 type likelihood function
likelihoodIidNormal     Normal / Gaussian Likelihood function
marginalLikelihood      Calcluated the marginal likelihood from a set
                        of MCMC samples
marginalPlot            Plot MCMC marginals
mergeChains             Merge Chains
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
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
updateProposalGenerator
                        To update settings of an existing proposal
                        genenerator
vsemC                   C version of the VSEM model
