S3 Generics for Bayesian Analyses
universals: S3 Generics for Bayesian Analyses
Effective Sampling Rate for Terms
Estimates
Number of Chains
Number of Dimensions
Number of Iterations
Number of Parameters
Number of Parameter Dimensions
Number of Samples
Number of Simulations
Number of Terms
Parameter Descriptions
Parameter Names
Parameter Dimensions
R-hat
Bind by Chains.
Bind Iterations
Collapse Chains
Converged
Converged Parameters
Converged Terms
Dimensions
Effective Sampling Rate
Effective Sampling Rate for Parameters
R-hat Parameters
R-hat Terms
Set Parameters
Split Chains
Provides S3 generic methods and some default implementations for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples. The purpose of 'universals' is to reduce package dependencies and conflicts. The 'nlist' package implements many of the methods for its 'nlist' class.
Useful links