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BayesianTools (version 0.1.6)

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

install.packages('BayesianTools')

Monthly Downloads

978

Version

0.1.6

License

GPL-3

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Repository

https://github.com/florianhartig/BayesianTools

Maintainer

Florian Hartig

Last Published

January 21st, 2019

Functions in BayesianTools (0.1.6)

Function to scale matrices

Plot MCMC marginals

createLikelihood

Creates a standardized likelihood class#'

sampleMetropolis

gets samples while adopting the MCMC proposal generator

Create a random radiation (PAR) time series

getDharmaResiduals

Creates a DHARMa object

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

Determine the groups of correlated parameters

sampleEquallySpaced

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

Very simple ecosystem model

calibrationTest

Simulation-based calibration tests

Extracts the sample from a bayesianOutput

Differential-Evolution MCMC zs

Deviance information criterion

getBlockSettings

getblockSettings

checkBayesianSetup

Checks if an object is of class 'BayesianSetup'

getCredibleIntervals

Calculate confidence region from an MCMC or similar sample

VSEMgetDefaults

returns the default values for the VSEM

createMcmcSamplerList

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

Produce multivariate normal proposal

Trace plot for MCMC class

marginalLikelihood

Calcluated the marginal likelihood from a set of MCMC samples

Determine the parameters in the block update

plotTimeSeriesResults

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

makeObjectClassCodaMCMC

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

Helper function for calculating beta

Differential-Evolution MCMC

The Metropolis Algorithm

marginalPlotDensity

Plot marginals as densities

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

calculates the Maxiumum APosteriori value (MAP)

plotTimeSeriesResiduals

Plots residuals of a time series

Function to get the setup from a bayesianOutput

createPosterior

Creates a standardized posterior class

Creates a standardized prior class

generateParallelExecuter

Factory to generate a parallel executer of an existing function

generateTestDensityMultiNormal

Multivariate normal likelihood

The Delayed Rejection Algorithm

createUniformPrior

Convenience function to create a simple uniform prior distribution

Wrapper for step function

The Adaptive Metropolis Algorithm

getPredictiveIntervals

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

getPossibleSamplerTypes

Returns possible sampler types

getPredictiveDistribution

Calculates predictive distribution based on the parameters

Adapts pCR values

marginalPlotViolin

Plot marginals as violin plot

mcmcMultipleChains

Run multiple chains

VSEMcreateLikelihood

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

metropolisRatio

Function to calculate the metropolis ratio

setupStartProposal

Help function to find starvalues and proposalGenerator settings

correlationPlot

Flexible function to create correlation density plots

Main wrapper function to start MCMCs, particle MCMCs and SMCs

getMetropolisDefaultSettings

Returns Metropolis default settings

testLinearModel

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

Main function that is executing and evaluating the moves

createBayesianSetup

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

Function to thin matrices

Calculates the marginal likelihood of a chain via bridge sampling

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

The Delayed Rejection Adaptive Metropolis Algorithm

Helper function for blow and hop moves

calculates the WAIC

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

testDensityBanana

Banana-shaped density function

AR1 type likelihood function

applySettingsDefault

Provides the default settings for the different samplers in runMCMC

createPriorDensity

Fits a density function to a multivariate sample

createBetaPrior

Convenience function to create a beta prior

createProposalGenerator

Factory that creates a proposal generator

likelihoodIidNormal

Normal / Gaussian Likelihood function

Calculate posterior volume

Function to combine chains

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

createMixWithDefaults

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

Helper function to create proposal

Convert coda::mcmc objects to BayesianTools::mcmcSampler

testDensityMultiNormal

3d Mutivariate Normal likelihood

testDensityNormal

Normal likelihood

Diagnostic Plot

gelmanDiagnostics

Runs Gelman Diagnotics over an BayesianOutput

testDensityInfinity

Test function infinity ragged

generateCRvalues

Generates matrix of CR values based on pCR

plotSensitivity

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

Calculates the panel combination for par(mfrow = )

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

Function to close cluster in BayesianSetup

Helper function for sum of x*x

updateProposalGenerator

To update settings of an existing proposal genenerator

C version of the VSEM model