kappalab: Non-additive measure and integral manipulation functions , Kappalab, which stands for ”laboratory for capacities”, is an S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation on a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Moebius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach grounded on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory. (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 25 articles , 1 standard article )

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  1. Agahi, Hamzeh; Mesiar, Radko; Motiee, Mehran: On Hoeffding and Bernstein type inequalities for sums of random variables in non-additive measure spaces and complete convergence (2016)
  2. Angilella, Silvia; Corrente, Salvatore; Greco, Salvatore: Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem (2015)
  3. Bortot, Silvia; Marques Pereira, Ricardo Alberto: The binomial Gini inequality indices and the binomial decomposition of welfare functions (2014)
  4. Greco, Salvatore; Mousseau, Vincent; Słowiński, Roman: Robust ordinal regression for value functions handling interacting criteria (2014)
  5. Wu, Jianzhang; Chen, Fang; Nie, Cuiping; Zhang, Qiang: Intuitionistic fuzzy-valued Choquet integral and its application in multicriteria decision making (2013)
  6. Zopounidis, Constantin; Doumpos, Michael: Multicriteria decision systems for financial problems (2013)
  7. Timonin, Mikhail: Maximization of the Choquet integral over a convex set and its application to resource allocation problems (2012)
  8. Doumpos, Michael; Zopounidis, Constantin: Preference disaggregation and statistical learning for multicriteria decision support: A review (2011)
  9. Fouchal, Hugo; Gandibleux, Xavier; Lehuédé, Fabien: A lower bound of the Choquet integral integrated within martins’ algorithm (2011)
  10. Meyer, Patrick; Ponthière, Grégory: Eliciting preferences on multiattribute societies with a Choquet integral (2011)
  11. Angilella, Silvia; Greco, Salvatore; Matarazzo, Benedetto: Non-additive robust ordinal regression: a multiple criteria decision model based on the Choquet integral (2010)
  12. Feng, Cheng-Min; Wu, Pei-Ju; Chia, Kai-Chieh: A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: a case study (2010)
  13. Grabisch, Michel; Labreuche, Christophe: A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid (2010)
  14. Wu, Jian-Zhang; Zhang, Qiang: 2-order additive fuzzy measure identification method based on diamond pairwise comparison and maximum entropy principle (2010)
  15. Beliakov, Gleb: Construction of aggregation functions from data using linear programming (2009)
  16. Bisdorff, Raymond; Meyer, Patrick; Veneziano, Thomas: Inverse analysis from a condorcet robustness denotation of valued outranking relations (2009)
  17. Kojadinovic, Ivan; Marichal, Jean-Luc: On the moments and distribution of discrete Choquet integrals from continuous distributions (2009)
  18. Meyer, Patrick: Progressive methods in multiple criteria decision analysis (2009)
  19. Prade, Henri; Rico, Agnès; Serrurier, Mathieu; Raufaste, Eric: Elicitating Sugeno integrals: methodology and a case study (2009)
  20. Waegeman, Willem; De Baets, Bernard; Boullart, Luc: Kernel-based learning methods for preference aggregation (2009)

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