Man pages for BayesianTools
General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

AMThe Adaptive Metropolis Algorithm
applySettingsDefaultProvides the default settings for the different samplers in...
checkBayesianSetupChecks if an object is of class 'BayesianSetup'
convertCodaConvert coda::mcmc objects to BayesianTools::mcmcSampler
correctThinChecks if thin is conistent with nTotalSamples samples and if...
correlationPlotFlexible function to create correlation density plots
createBayesianSetupCreates a standardized collection of prior, likelihood and...
createBetaPriorConvenience function to create a beta prior
createLikelihoodCreates a standardized likelihood class#'
createMcmcSamplerListConvenience function to create an object of class...
createPosteriorCreates a standardized posterior class
createPriorCreates a standardized prior class
createPriorDensityFits a density function to a multivariate sample
createProposalGeneratorFactory that creates a proposal generator
createSmcSamplerListConvenience function to create an object of class...
createTruncatedNormalPriorConvenience function to create a truncated normal prior
createUniformPriorConvenience function to create a simple uniform prior...
DEDifferential-Evolution MCMC
DEzsDifferential-Evolution MCMC zs
DICDeviance information criterion
DRThe Delayed Rejection Algorithm
DRAMThe Delayed Rejection Adaptive Metropolis Algorithm
gelmanDiagnosticsRuns Gelman Diagnotics over an BayesianOutput
generateParallelExecuterFactory to generate a parallel executer of an existing...
generateTestDensityMultiNormalMultivariate normal likelihood
getCredibleIntervalsCalculate confidence region from an MCMC or similar sample
getDharmaResidualsCreates a DHARMa object
getPanelsCalculates the panel combination for par(mfrow = )
getPossibleSamplerTypesReturns possible sampler types
getPredictiveDistributionCalculates predictive distribution based on the parameters
getPredictiveIntervalsCalculates Bayesian credible (confidence) and predictive...
getSampleExtracts the sample from a bayesianOutput
getVolumeCalculate posterior volume
GOFStandard GOF metrics Startvalues for sampling with nrChains >...
likelihoodAR1AR1 type likelihood function
likelihoodIidNormalNormal / Gaussian Likelihood function
logSumExpFunktion to compute log(sum(exp(x))
MThe Metropolis Algorithm
MAPcalculates the Maxiumum APosteriori value (MAP)
marginalLikelihoodCalcluated the marginal likelihood from a set of MCMC samples
marginalPlotPlot MCMC marginals
mcmcMultipleChainsRun multiple chains
MetropolisCreates a Metropolis-type MCMC with options for covariance...
metropolisRatioFunction to calculate the metropolis ratio
package-deprecatedAllows to mix a given parameter vector with a default...
plotSensitivityPerforms a one-factor-at-a-time sensitivity analysis for the...
plotTimeSeriesPlots a time series, with the option to include confidence...
plotTimeSeriesResidualsPlots residuals of a time series
plotTimeSeriesResultsCreates a time series plot typical for an MCMC / SMC fit
runMCMCMain wrapper function to start MCMCs, particle MCMCs and SMCs
sampleEquallySpacedGets n equally spaced samples (rows) from a matrix or vector
sampleMetropolisgets samples while adopting the MCMC proposal generator
setupStartProposalHelp function to find starvalues and proposalGenerator...
smcSamplerSMC sampler
stopParallelFunction to close cluster in BayesianSetup
testDensityBananaBanana-shaped density function
testDensityInfinityTest function infinity ragged
testDensityMultiNormal3d Mutivariate Normal likelihood
testDensityNormalNormal likelihood
testLinearModelFake model, returns a ax + b linear response to 2-param...
tracePlotTrace plot for MCMC class
TwalkT-walk MCMC
updateProposalGeneratorTo update settings of an existing proposal genenerator
VSEMVery simple ecosystem model
vsemCC version of the VSEM model
VSEMcreateLikelihoodCreate an example dataset, and from that a likelihood or...
VSEMcreatePARCreate a random radiation (PAR) time series
VSEMgetDefaultsreturns the default values for the VSEM
WAICcalculates the WAIC
BayesianTools documentation built on Aug. 2, 2017, 5:02 p.m.