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BayesianTools (version 0.1.8)

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

Description

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

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install.packages('BayesianTools')

Monthly Downloads

998

Version

0.1.8

License

GPL-3

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Repository

https://github.com/florianhartig/BayesianTools

Maintainer

Florian Hartig

Last Published

January 30th, 2023

Functions in BayesianTools (0.1.8)

calculates the WAIC

applySettingsDefault

Provides the default settings for the different samplers in runMCMC

calibrationTest

Simulation-based calibration tests

Helper function for blow and hop moves

checkBayesianSetup

Checks if an object is of class 'BayesianSetup'

Calculates the marginal likelihood of a chain via bridge sampling

createPriorDensity

Fits a density function to a multivariate sample

Convert coda::mcmc objects to BayesianTools::mcmcSampler

createLikelihood

Creates a standardized likelihood class#'

createMcmcSamplerList

Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers

createProposalGenerator

Factory that creates a proposal generator

Function to combine chains

Helper function for calculating beta

Create a random radiation (PAR) time series

Checks if thin is consistent with nTotalSamples samples and if not corrects it.

correlationPlot

Flexible function to create correlation density plots

VSEMgetDefaults

returns the default values for the VSEM

createBayesianSetup

Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.

createSmcSamplerList

Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers

getBlockSettings

getblockSettings

generateParallelExecuter

Factory to generate a parallel executor of an existing function

generateTestDensityMultiNormal

Multivariate normal likelihood

createBetaPrior

Convenience function to create a beta prior

Determine the parameters in the block update

createUniformPrior

Convenience function to create a simple uniform prior distribution

getPredictiveIntervals

Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample

getPredictiveDistribution

Calculates predictive distribution based on the parameters

getDharmaResiduals

Creates a DHARMa object

createTruncatedNormalPrior

Convenience function to create a truncated normal prior

getCredibleIntervals

Calculate confidence region from an MCMC or similar sample

getPossibleSamplerTypes

Returns possible sampler types

Creates a standardized prior class

gelmanDiagnostics

Gelman Diagnostics

Function to get the setup from a bayesianOutput

createPosterior

Creates a standardized posterior class

Extracts the sample from a bayesianOutput

Produce multivariate normal proposal

generateCRvalues

Generates matrix of CR values based on pCR

marginalPlotDensity

Plot marginals as densities

mcmcMultipleChains

Run multiple chains

likelihoodIidNormal

Normal / Gaussian Likelihood function

Calculate posterior volume

marginalPlotViolin

Plot marginals as violin plot

Diagnostic Plot

AR1 type likelihood function

plotSensitivity

Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range.

Plot MCMC marginals

marginalLikelihood

Calcluated the marginal likelihood from a set of MCMC samples

Plots a time series, with the option to include confidence and prediction band

metropolisRatio

Function to calculate the metropolis ratio

Funktion to compute log(sum(exp(x))

createMixWithDefaults

Allows to mix a given parameter vector with a default parameter vector

plotTimeSeriesResults

Creates a time series plot typical for an MCMC / SMC fit

Helper function to create proposal

sampleEquallySpaced

Gets n equally spaced samples (rows) from a matrix or vector

makeObjectClassCodaMCMC

Helper function to change an object to a coda mcmc class,

Main wrapper function to start MCMCs, particle MCMCs and SMCs

Function to scale matrices

testDensityMultiNormal

3d Mutivariate Normal likelihood

testDensityNormal

Normal likelihood

testDensityGelmanMeng

GelmanMeng test function

testDensityInfinity

Test function infinity ragged

setupStartProposal

Help function to find starvalues and proposalGenerator settings

updateProposalGenerator

To update settings of an existing proposal genenerator

sampleMetropolis

gets samples while adopting the MCMC proposal generator

Determine the groups of correlated parameters

Trace plot for MCMC class

Function to close cluster in BayesianSetup

testDensityBanana

Banana-shaped density function

plotTimeSeriesResiduals

Plots residuals of a time series

Helper function for sum of x*x

C version of the VSEM model

testLinearModel

Fake model, returns a ax + b linear response to 2-param vector

Function to thin matrices

The Delayed Rejection Algorithm

Differential-Evolution MCMC zs

The Adaptive Metropolis Algorithm

The Delayed Rejection Adaptive Metropolis Algorithm

Adapts pCR values

Deviance information criterion

The Metropolis Algorithm

calculates the Maxiumum APosteriori value (MAP)

Very simple ecosystem model

VSEMcreateLikelihood

Create an example dataset, and from that a likelihood or posterior for the VSEM model

Standard GOF metrics Startvalues for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide a list

Main function that is executing and evaluating the moves

Differential-Evolution MCMC

Wrapper for step function

Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering