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:
term
is a single real or integer value
.par
(short for parameter) is a numeric object of terms.chains
of the same length
(number of iterations
).simulations
is the product of the number of
iterations and the number of chains.samples
is the product of the number of simulations
and the number of terms
.The ‘nlist’ package implements many of the methods for its ‘nlists’ class.
To install the latest release from CRAN
install.packages("universals")
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 roxygen2
code:
#' @importFrom universals pars
#' @export
universals::pars
Please report any issues.
Pull requests are always welcome.
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.
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