BayesianTools-package | BayesianTools |

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 | 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 |

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 |

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 |