Differential-Evolution MCMC
Differential-Evolution MCMC zs
The Adaptive Metropolis Algorithm
BayesianTools
Deviance information criterion
The Delayed Rejection Algorithm
DREAMzs
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
DREAM
calculates the WAIC
Provides the default settings for the different samplers in runMCMC
Creates a standardized likelihood class#'
Creates a Metropolis-type MCMC with options for covariance adaptatin, delayed rejection, Metropolis-within-Gibbs, and tempering
T-walk MCMC
Creates a standardized collection of prior, likelihood and posterior functions, including error checks etc.
Convenience function to create a beta prior
Fits a density function to a multivariate sample
Factory that creates a proposal generator
Funktion to compute log(sum(exp(x))
Calcluated the marginal likelihood from a set of MCMC samples
Very simple ecosystem model
Create an example dataset, and from that a likelihood or posterior for the VSEM model
Checks if an object is of class 'BayesianSetup'
Convert coda::mcmc objects to BayesianTools::mcmcSampler
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
Calculates Bayesian credible (confidence) and predictive intervals based on parameter sample
Convenience function to create a simple uniform prior distribution
Runs Gelman Diagnotics over an BayesianOutput
AR1 type likelihood function
Normal / Gaussian Likelihood function
Factory to generate a parallel executer of an existing function
Plots residuals of a time series
Creates a time series plot typical for an MCMC / SMC fit
3d Mutivariate Normal likelihood
Create a random radiation (PAR) time series
returns the default values for the VSEM
Creates a standardized posterior class
Function to calculate the metropolis ratio
Allows to mix a given parameter vector with a default parameter vector
SMC sampler
Function to close cluster in BayesianSetup
Normal likelihood
To update settings of an existing proposal genenerator
C version of the VSEM model
Main wrapper function to start MCMCs, particle MCMCs and SMCs
Gets n equally spaced samples (rows) from a matrix or vector
Fake model, returns a ax + b linear response to 2-param vector
Trace plot for MCMC class
Creates a standardized prior class
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 = )
Returns possible sampler types
Plot MCMC marginals
Run multiple chains
gets samples while adopting the MCMC proposal generator
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.
Flexible function to create correlation density plots
Calculate confidence region from an MCMC or similar sample
Creates a DHARMa object
Extracts the sample from a bayesianOutput
Calculate posterior volume
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
Banana-shaped density function
Test function infinity ragged