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

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

## Documentation for package ‘BayesianTools’ version 0.1.9

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

## Help Pages

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 user-defined 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 samples from a bayesianOutput
getSample.data.frame | Extracts samples from a bayesianOutput
getSample.double | Extracts samples from a bayesianOutput
getSample.integer | Extracts samples from a bayesianOutput
getSample.list | Extracts samples from a bayesianOutput
getSample.matrix | Extracts samples from a bayesianOutput
getSample.MCMC | Extracts samples from a bayesianOutput
getSample.mcmc | Extracts samples from a bayesianOutput
getSample.mcmc.list | Extracts samples from a bayesianOutput
getSample.mcmcSampler | Extracts samples from a bayesianOutput
getSample.mcmcSamplerList | Extracts samples from a bayesianOutput
getSample.MCMC_refClass | Extracts samples from a bayesianOutput
getSample.smcSampler | Extracts samples 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 Maximum APosteriori value (MAP)
marginalLikelihood | Calculated 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 adaptation, delayed rejection, Metropolis-within-Gibbs, and tempering
plot.mcmcSampler | Plots of MCMC output
plot.mcmcSamplerList | Plots of MCMC output
plot.smcSampler | Plots of smcSampler output
plot.smcSamplerList | Plots of smcSampler output
plotDiagnostic | Diagnostic Plot
plotSensitivity | One-factor-at-a-time sensitivity plot
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
print.BayesianSetup | Print an object of BayesianSetup
print.mcmcSampler | Prints MCMC output
print.mcmcSamplerList | Prints MCMC output
print.prior | Print object of prior class
print.smcSampler | Print of smcSampler output
print.smcSamplerList | Print of smcSampler output
runMCMC | Main wrapper function to start MCMCs, particle MCMCs and SMCs
smcSampler | SMC sampler
stopParallel | Function to close cluster in BayesianSetup
summary.mcmcSampler | Summmary of MCMC output
summary.mcmcSamplerList | Summmary of MCMC output
summary.smcSampler | Summary for class 'smcSampler'
summary.smcSamplerList | Summary for class 'smcSampler'
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