person("Samuel E.", "Buttrey", role = "ctb"),
person("Stefan I.", "Larimore", role = "ctb"),
person("Timothy A.", "Davis", role = "ctb"),
person("John", "Gilbert", role = "ctb"),
person("Peter", "Notebaert", role = "ctb") ,
person("Richard", "Stallman", role = "ctb") ,
person("Jeroen", "Dirks", role = "ctb") )
Description: Various tools for handling fuzzy measures, calculating Shapley value and interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment
Standards and Technology, Free Software Foundation, Inc.

person("Jeroen", "Dirks", role = "ctb"),
person("Daniela L.", "Calderon", role = "ctb") )
is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following:
[2] G. Beliakov, H. Bustince, T. Calvo, 'A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3).
[3] G. Beliakov, S. James, J-Z. Wu, 'Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).

person("Alban", "Siffer", email="alban.siffer@irisa.fr", role= c("aut","cre")),
person("Amossys", email="contact@amossys.fr", role = c("cph", "fnd")))
Description: The basic algorithm to perform the folding test of unimodality.
Given a dataset X (d dimensional, n samples), the test checks whether the
distribution of the data are rather unimodal or rather multimodal. This
package stems from the following research publication:
Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët.
"Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International
Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.

Title: Random Forests, Linear Trees, and Gradient Boosting for
person("Sören", "Künzel", role = "aut"),
person("Theo", "Saarinen", role = c("aut","cre"), email = "theo_s@berkeley.edu"),
person("Simon", "Walter", role = "aut"),
person("Jasjeet", "Sekhon", role = "aut")
Description: Provides fast implementations of Honest Random Forests,
Gradient Boosting, and Linear Random Forests, with an emphasis on inference
and interpretability. Additionally contains methods for variable
importance, out-of-bag prediction, regression monotonicity, and
several methods for missing data imputation. Soren R. Kunzel,
Theo F. Saarinen, Edward W. Liu, Jasjeet S. Sekhon (2019) <arXiv:1906.06463>.
Imports: Rcpp (>= 0.12.9), parallel, methods, visNetwork, glmnet (>=