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| BayesianTools-package | BayesianTools |
| AM | The Adaptive Metropolis Algorithm |
| 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 |
| 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 |
| DR | The Delayed Rejection Algorithm |
| DRAM | The Delayed Rejection Adaptive Metropolis Algorithm |
| 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)) |
| M | The Metropolis Algorithm |
| 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 |
| 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 |