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

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Documentation for package ‘BayesianTools’ version 0.1.8

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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
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 the sample from a bayesianOutput 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
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