universals 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 methods are primarily designed to be used for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples but many can also be used for Maximum Likelihood (ML) and other types of analyses.
The names of the functions are based on the following definitions/concepts:
termis a single real or integer
par(short for parameter) is a numeric object of terms.
chainsof the same length (number of
simulationsis the product of the number of iterations and the number of chains.
samplesis the product of the number of simulations and the number of
The ‘nlist’ package implements many of the methods for its ‘nlists’ class.
To install the latest release from CRAN
To install the developmental version from GitHub
# install.packages("remotes") remotes::install_github("poissonconsulting/universals")
universals is designed to be used by package developers.
It is recommended to import and re-export the generics of interest. For example, to provide a method for the S3
pars() method, use the following
#' @importFrom universals pars #' @export universals::pars
Please note that the universals project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.