statistical programming, with special emphasis on R/S3 language
extensions, including extensions for environment-based

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")))
Description: 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.
Suggests: knitr, rmarkdown, xtable, testthat (>= 3.0.0), covr
Author: Philipp Külpmann [aut, cre] (<https://orcid.org/0000-0003-3757-7172>),

Title: Optimized Automated Gaussian Mixture Assessment
Author: Jorn Lotsch [aut,cre] (<https://orcid.org/0000-0002-5818-6958>),
Description: 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.
cluster, mixtools, grDevices, methods, foreach, stats, utils,
rlang, ggplot2, parallel, caTools, dplyr, mclust, mixAK,

Description: 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. A detailed description of the methods ca ben found in Lotsch, J., Malkusch, S. and A. Ultsch. (2022) <doi:10.1016/j.imu.2022.101113>.

Title: An Implementation of Ordinal Pattern Analysis
Authors@R: person("Timothy", "Beechey", email = "tim.beechey@protonmail.com",
Description: Quantifyies hypothesis to data fit for repeated measures
observations and time series data, as described by Thorngate (1987)
<doi:10.1177/2158244015604192>. Hypothesis and data are encoded as
pairwise relative orderings which are then compared to determine the
percentage of orderings in the data that are matched by the hypothesis.
Imports: Rcpp, assertthat, combinat, ggplot2
Author: Timothy Beechey [aut, cre] (<https://orcid.org/0000-0001-8858-946X>)