Create simple, hassle-free classes with reference semantics
similar to 'RefClass' or 'R6' but relying on S3 methods. "oops" class
instances tend to be lighter weight and faster to create. Creating a
class is as easy creating a list, while generating an instance is a
simple function call. Support for inheritance and fixed field classes.

An R package designed to support object oriented
statistical programming, especially by extending S3
capabilities, providing pointer and component objects, and
providing basic support for symbolic-numeric statistical
programming.

A platform for, and collection of tools for, object
oriented statistical programming in R.

A platform and collection of tools for, object oriented
statistical programming, with special emphasis on R (S3)
language extensions, including extensions for environment-based
programming and object-functional programming. Note that many
of the features in this package are experimental.

A platform and collection of tools for, object oriented
statistical programming, with special emphasis on R/S3 language
extensions, including extensions for environment-based
programming and object-functional programming. Note that many
of the features in this package are experimental.

c(
person("Philipp", "Külpmann", email = "philipp@kuelpmann.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3757-7172")),
person("Panagiota", "Charalampidou", role = c("aut")),
person(given = "University of Vienna", role = c("cph")))
Implements the out-of-treatment testing from Kuelpmann and Kuzmics (2020) <doi:10.2139/ssrn.3441675> based on the Vuong Test introduced in Vuong (1989) <doi:10.2307/1912557>.
Out-of treatment testing allows for a direct, pairwise likelihood comparison of theories, calibrated with pre-existing data.
knitr, rmarkdown, xtable, testthat (>= 3.0.0), covr
Philipp Külpmann [aut, cre] (<https://orcid.org/0000-0003-3757-7172>),
Panagiota Charalampidou [aut],
University of Vienna [cph]

Optimized Automated Gaussian Mixture Assessment
Jorn Lotsch [aut,cre] (<https://orcid.org/0000-0002-5818-6958>),
Sebastian Malkusch [aut] (<https://orcid.org/0000-0001-6766-140X>)
Necessary functions for optimized automated evaluation of the number and parameters of Gaussian mixtures in one-dimensional data. Various methods are available for parameter estimation and for determining the number of modes in the mixture.
AdaptGauss, DataVisualizations, DistributionOptimization,
cluster, mixtools, grDevices, methods, foreach, stats, utils,
rlang, ggplot2, parallel, caTools, dplyr, mclust, mixAK,
multimode, NbClust, ClusterR, doParallel