Function to scale matrices
Plot MCMC marginals
Creates a standardized likelihood class#'
gets samples while adopting the MCMC proposal generator
Create a random radiation (PAR) time series
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
Gets n equally spaced samples (rows) from a matrix or vector
Very simple ecosystem model
Simulation-based calibration tests
Extracts the sample from a bayesianOutput
Differential-Evolution MCMC zs
Deviance information criterion
getblockSettings
Checks if an object is of class 'BayesianSetup'
Calculate confidence region from an MCMC or similar sample
returns the default values for the VSEM
Convenience function to create an object of class mcmcSamplerList from a list of mcmc samplers
BayesianTools
Produce multivariate normal proposal
Trace plot for MCMC class
Calcluated the marginal likelihood from a set of MCMC samples
Determine the parameters in the block update
Creates a time series plot typical for an MCMC / SMC fit
Helper function to change an object to a coda mcmc class,
T-walk MCMC
Helper function for calculating beta
Differential-Evolution MCMC
The Metropolis Algorithm
Plot marginals as densities
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)
Plots residuals of a time series
Function to get the setup from a bayesianOutput
Creates a standardized posterior class
Creates a standardized prior class
Factory to generate a parallel executer of an existing function
generateTestDensityMultiNormal
Multivariate normal likelihood
The Delayed Rejection Algorithm
Convenience function to create a simple uniform prior distribution
Wrapper for step function
The Adaptive Metropolis Algorithm
Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample
Returns possible sampler types
getPredictiveDistribution
Calculates predictive distribution based on the parameters
Adapts pCR values
Plot marginals as violin plot
Run multiple chains
Create an example dataset, and from that a likelihood or posterior for the VSEM model
Merge Chains
Function to calculate the metropolis ratio
Help function to find starvalues and proposalGenerator settings
Flexible function to create correlation density plots
Main wrapper function to start MCMCs, particle MCMCs and SMCs
getMetropolisDefaultSettings
Returns Metropolis default settings
SMC sampler
Fake model, returns a ax + b linear response to 2-param vector
DREAM
DREAMzs
Main function that is executing and evaluating the moves
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.
Banana-shaped density function
AR1 type likelihood function
Provides the default settings for the different samplers in runMCMC
Fits a density function to a multivariate sample
Convenience function to create a beta prior
Factory that creates a proposal generator
factorMatrice
Normal / Gaussian Likelihood function
Calculate posterior volume
Function to combine chains
Funktion to compute log(sum(exp(x))
Allows to mix a given parameter vector with a default parameter vector
Helper function to create proposal
Convert coda::mcmc objects to BayesianTools::mcmcSampler
Rescale
3d Mutivariate Normal likelihood
Normal likelihood
Diagnostic Plot
Runs Gelman Diagnotics over an BayesianOutput
Test function infinity ragged
Generates matrix of CR values based on pCR
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
To update settings of an existing proposal genenerator
C version of the VSEM model