calculates the WAIC
Provides the default settings for the different samplers in runMCMC
Simulation-based calibration tests
Helper function for blow and hop moves
Checks if an object is of class 'BayesianSetup'
Calculates the marginal likelihood of a chain via bridge sampling
Fits a density function to a multivariate sample
Convert coda::mcmc objects to BayesianTools::mcmcSampler
Creates a standardized likelihood class#'
Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
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.
Flexible function to create correlation density plots
returns the default values for the VSEM
Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.
Convenience function to create an object of class SMCSamplerList from a list of mcmc samplers
getblockSettings
Factory to generate a parallel executor of an existing function
generateTestDensityMultiNormal
Multivariate normal likelihood
Convenience function to create a beta prior
Determine the parameters in the block update
Convenience function to create a simple uniform prior distribution
Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample
getPanels
getPredictiveDistribution
Calculates predictive distribution based on the parameters
Creates a DHARMa object
createTruncatedNormalPrior
Convenience function to create a truncated normal prior
Calculate confidence region from an MCMC or similar sample
Returns possible sampler types
Creates a standardized prior class
Gelman Diagnostics
Function to get the setup from a bayesianOutput
Creates a standardized posterior class
Extracts the sample from a bayesianOutput
Produce multivariate normal proposal
Generates matrix of CR values based on pCR
factorMatrice
Plot marginals as densities
Run multiple chains
Normal / Gaussian Likelihood function
Calculate posterior volume
Plot marginals as violin plot
Diagnostic Plot
AR1 type likelihood function
Performs a one-factor-at-a-time sensitivity analysis for the posterior of a given bayesianSetup within the prior range.
Plot MCMC marginals
Calcluated the marginal likelihood from a set of MCMC samples
Plots a time series, with the option to include confidence and prediction band
Function to calculate the metropolis ratio
Merge Chains
Funktion to compute log(sum(exp(x))
Allows to mix a given parameter vector with a default parameter vector
SMC sampler
Creates a time series plot typical for an MCMC / SMC fit
Helper function to create proposal
Gets n equally spaced samples (rows) from a matrix or vector
Helper function to change an object to a coda mcmc class,
Main wrapper function to start MCMCs, particle MCMCs and SMCs
Rescale
Function to scale matrices
3d Mutivariate Normal likelihood
Normal likelihood
GelmanMeng test function
Test function infinity ragged
Help function to find starvalues and proposalGenerator settings
To update settings of an existing proposal genenerator
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
Banana-shaped density function
Plots residuals of a time series
Helper function for sum of x*x
C version of the VSEM model
Fake model, returns a ax + b linear response to 2-param vector
Function to thin matrices
The Delayed Rejection Algorithm
DREAMzs
DREAM
Differential-Evolution MCMC zs
BayesianTools
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
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
T-walk MCMC
Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering