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BayesianTools (version 0.1.1)

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

791

Version

0.1.1

License

CC BY-SA 4.0

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Repository

https://github.com/florianhartig/BayesianTools

Maintainer

Florian Hartig

Last Published

April 5th, 2017

Functions in BayesianTools (0.1.1)

Standard GOF metrics

The Delayed Rejection Adaptive Metropolis Algorithm

Differential-Evolution MCMC

Differential-Evolution MCMC zs

Deviance information criterion

The Delayed Rejection Algorithm

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

Very simple ecosystem model

VSEMcreateLikelihood

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

createPosterior

Creates a standardized posterior class

Creates a standardized prior class

createUniformPrior

Convenience function to create a simple uniform prior distribution

gelmanDiagnostics

Runs Gelman Diagnotics over an BayesianOutput

likelihoodIidNormal

Normal / Gaussian Likelihood function

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

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

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

Main wrapper function to start MCMCs, particle MCMCs and SMCs

sampleMetropolis

gets samples while adopting the MCMC proposal generator

testDensityInfinity

Test function infinity ragged

The Adaptive Metropolis Algorithm

Create a random radiation (PAR) time series

VSEMgetDefaults

returns the default values for the VSEM

The Metropolis Algorithm

calculates the Maxiumum APosteriori value (MAP)

mcmcMultipleChains

Run multiple chains

metropolisRatio

Funktion to calculate the metropolis ratio

createBetaPrior

Convenience function to create a beta prior

createLikelihood

Creates a standardized likelihood class

generateParallelExecuter

Factory to generate a parallel executer of an existing function

generateTestDensityMultiNormal

Multivariate normal likelihood

getPossibleSamplerTypes

Returns possible sampler types

getPredictiveDistribution

Calculates predictive distribution based on the parameters

setupStartProposal

Help function to find starvalues and proposalGenerator settings

testDensityNormal

Normal likelihood

Trace plot for MCMC class

updateProposalGenerator

To update settings of an existing proposal genenerator

checkBayesianSetup

Checks if an object is of class 'BayesianSetup'

Convert coda::mcmc objects to BayesianTools::mcmcSampler

createPriorDensity

Fits a density function to a multivariate sample

createProposalGenerator

Factory that creates a proposal generator

getPredictiveIntervals

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

Extracts the sample from a bayesianOutput

calculates the WAIC

applySettingsDefault

Provides the default settings for the different samplers in runMCMC

correlationPlot

Flexible function to create correlation density plots

createBayesianSetup

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

Calculate posterior volume

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.

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

testLinearModel

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

Function to close cluster in BayesianSetup

testDensityBanana

Banana-shaped density function

testDensityMultiNormal

3d Mutivariate Normal likelihood

getCredibleIntervals

Calculate confidence region from an MCMC or similar sample

Calculates the panel combination for par(mfrow = )

marginalLikelihood

Calcluated the marginal likelihood from a set of MCMC samples

Plot MCMC marginals

plotTimeSeriesResiduals

Plots residuals of a time series

plotTimeSeriesResults

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

C version of the VSEM model