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BayesianTools (version 0.1.3)

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')

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1,156

Version

0.1.3

License

CC BY-SA 4.0

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Repository

https://github.com/florianhartig/BayesianTools

Maintainer

Florian Hartig

Last Published

August 2nd, 2017

Functions in BayesianTools (0.1.3)

Differential-Evolution MCMC

Differential-Evolution MCMC zs

The Adaptive Metropolis Algorithm

Deviance information criterion

The Delayed Rejection Algorithm

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

calculates the WAIC

applySettingsDefault

Provides the default settings for the different samplers in runMCMC

createLikelihood

Creates a standardized likelihood class#'

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

createBayesianSetup

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

createBetaPrior

Convenience function to create a beta prior

createPriorDensity

Fits a density function to a multivariate sample

createProposalGenerator

Factory that creates a proposal generator

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

marginalLikelihood

Calcluated the marginal likelihood from a set of MCMC samples

Very simple ecosystem model

VSEMcreateLikelihood

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

checkBayesianSetup

Checks if an object is of class 'BayesianSetup'

Convert coda::mcmc objects to BayesianTools::mcmcSampler

createMcmcSamplerList

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

generateTestDensityMultiNormal

Multivariate normal likelihood

getPredictiveDistribution

Calculates predictive distribution based on the parameters

getPredictiveIntervals

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

createUniformPrior

Convenience function to create a simple uniform prior distribution

gelmanDiagnostics

Runs Gelman Diagnotics over an BayesianOutput

AR1 type likelihood function

likelihoodIidNormal

Normal / Gaussian Likelihood function

generateParallelExecuter

Factory to generate a parallel executer of an existing function

plotTimeSeriesResiduals

Plots residuals of a time series

plotTimeSeriesResults

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

testDensityMultiNormal

3d Mutivariate Normal likelihood

Create a random radiation (PAR) time series

VSEMgetDefaults

returns the default values for the VSEM

createPosterior

Creates a standardized posterior class

metropolisRatio

Function to calculate the metropolis ratio

createMixWithDefaults

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

Function to close cluster in BayesianSetup

testDensityNormal

Normal likelihood

updateProposalGenerator

To update settings of an existing proposal genenerator

C version of the VSEM model

Main wrapper function to start MCMCs, particle MCMCs and SMCs

sampleEquallySpaced

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

testLinearModel

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

Trace plot for MCMC class

Creates a standardized prior class

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 panel combination for par(mfrow = )

getPossibleSamplerTypes

Returns possible sampler types

Plot MCMC marginals

mcmcMultipleChains

Run multiple chains

sampleMetropolis

gets samples while adopting the MCMC proposal generator

setupStartProposal

Help function to find starvalues and proposalGenerator settings

The Metropolis Algorithm

calculates the Maxiumum APosteriori value (MAP)

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

correlationPlot

Flexible function to create correlation density plots

getCredibleIntervals

Calculate confidence region from an MCMC or similar sample

getDharmaResiduals

Creates a DHARMa object

Extracts the sample from a bayesianOutput

Calculate posterior volume

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

testDensityBanana

Banana-shaped density function

testDensityInfinity

Test function infinity ragged