The Adaptive Metropolis Algorithm
Creates a standardized prior class
Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers
Convenience function to create a beta prior
Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
Factory that creates a proposal generator
Fits a density function to a multivariate sample
Creates a standardized posterior class
createTruncatedNormalPrior
Convenience function to create a truncated normal prior
Creates a standardized likelihood class
Allows to mix a given parameter vector with a default parameter vector
The Delayed Rejection Algorithm
Runs Gelman Diagnotics over an Bayesianutput
The Delayed Rejection Adaptive Metropolis Algorithm
Convenience function to create a simple uniform prior distribution
Differential-Evolution MCMC
Deviance information criterion
Differential-Evolution MCMC zs
DREAM
DREAMzs
factorMatrice
Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample
getPredictiveDistribution
Calculates predictive distribution based on the parameters
Factory to generate a parallel executer of an existing function
Generates matrix of CR values based on pCR
calculates the Maxiumum APosteriori value (MAP)
Calcluated the marginal likelihood from a set of MCMC samples
getblockSettings
Calculate confidence region from an MCMC or similar sample
Returns possible sampler types
Calculates the panel combination for par(mfrow = )
Funktion to compute log(sum(exp(x))
Normal / Gaussian Likelihood function
Helper function for sum of x*x
Plots residuals of a time series
Creates a time series plot typical for an MCMC / SMC fit
Run multiple chains
generateTestDensityMultiNormal
Multivariate normal likelihood
Determine the parameters in the block update
Plot MCMC marginals
Function to close cluster in BayesianSetup
Main function that is executing and evaluating the moves
The Metropolis Algorithm
Determine the groups of correlated parameters
Help function to find starvalues and proposalGenerator settings
To update settings of an existing proposal genenerator
Wrapper for step function
Helper function to create proposal
Main wrapper function to start MCMCs, particle MCMCs and SMCs
Helper function to change an object to a coda mcmc class,
SMC sampler
Extracts the sample from a bayesianOutput
Test function infinity ragged
Create a random radiation (PAR) time series
returns the default values for the VSEM
Produce multivariate normal proposal
Fake model, returns a ax + b linear response to 2-param vector
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
Function to thin matrices
Banana-shaped density function
C version of the VSEM model
Create an example dataset, and from that a likelihood or posterior for the VSEM model
Helper function for blow and hop moves
Normal likelihood
gets samples while adopting the MCMC proposal generator
Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering
Funktion to calculate the metropolis ratio
3d Mutivariate Normal likelihood
AR1 type likelihood function
Function to scale matrices
T-walk MCMC
Trace plot for MCMC class
Function to combine chains
Convert coda::mcmc objects to BayesianTools::mcmcSampler
Checks if an object is of class 'BayesianSetup'
Provides the default settings for the different samplers in runMCMC
Helper function for calculating beta
Adapts pCR values
Flexible function to create correlation density plots
BayesianTools
Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.
Very simple ecosystem model
calculates the WAIC