agop - Online Manual

agop-package {agop}R Documentation

Aggregation Operators and Preordered Sets Package for R


“The process of combining several numerical values into a single representative one is called aggregation, and the numerical function performing this process is called aggregation function. This simple definition demonstrates the size of the field of application of aggregation: applied mathematics (e.g. probability, statistics, decision theory), computer science (e.g. artificial intelligence, operation research), as well as many applied fields (economics and finance, pattern recognition and image processing, data fusion, multicriteria decision making, automated reasoning etc.). Although history of aggregation is probably as old as mathematics (think of the arithmetic mean), its existence has reminded underground till only recent (...).” (Grabisch et al., 2009, p. xiii)


agop is an open source (LGPL 3) package for R, to which anyone can contribute. It started as a fork of the CITAN package (Gagolewski, 2011).

For more information refer to the Package Vignette.

Package Facilities


Marek Gagolewski [aut,cre],
Anna Cena [ctb]

Keywords: aggregation, bibliometrics, scientometrics, scientific impact, webometrics, preorders, binary relations, means, OWA, OWMax, OWMin, Hirsch's h-index, Egghe's g-index, variance, spread, decision making, fuzzy logic.

Acknowledgments: The development of the package in March-June 2013 was partially supported by the European Union from resources of the European Social Fund, Project PO KL “Information technologies: Research and their interdisciplinary applications”, agreement UDA-POKL.04.01.01-00-051/10-00.


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Cena A., Gagolewski M., OM3: Ordered maxitive, minitive, and modular aggregation operators – axiomatic and probabilistic properties in an arity-monotonic setting, Fuzzy Sets and Systems, 2014, doi:10.1016/j.fss.2014.04.001.

Cena A., Gagolewski M., OM3: ordered maxitive, minitive, and modular aggregation operators - Part I: Axiomatic analysis under arity-dependence, In: Bustince H. et al (Eds.), Aggregation Functions in Theory and in Practise (AISC 228), Springer-Verlag, Heidelberg, 2013, pp. 93-103.

Cena A., Gagolewski M., OM3: ordered maxitive, minitive, and modular aggregation operators - Part II: A simulation study, In: Bustince H. et al (Eds.), Aggregation Functions in Theory and in Practise (AISC 228), Springer-Verlag, Heidelberg, 2013, pp. 105-115.

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Gagolewski M., Spread measures and their relation to aggregation functions, European Journal of Operational Research, 2014, doi:10.1016/j.ejor.2014.08.034.

Gagolewski M., Scientific Impact Assessment Cannot be Fair, Journal of Informetrics 7(4), 2013, pp. 792-802.

Gagolewski M., On the Relationship Between Symmetric Maxitive, Minitive, and Modular Aggregation Operators, Information Sciences 221, 2013, pp. 170-180.

Gagolewski M., Grzegorzewski P., Possibilistic Analysis of Arity-Monotonic Aggregation Operators and Its Relation to Bibliometric Impact Assessment of Individuals, International Journal of Approximate Reasoning 52(9), 2011, pp. 1312-1324.

Gagolewski M., Mesiar R., Aggregating Different Paper Quality Measures with a Generalized h-index, Journal of Informetrics 6(4), 2012, pp. 566-579.

Gagolewski M., Mesiar R., Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem, Information Sciences 263, 2014, pp. 166-174.

Gagolewski M., Bibliometric Impact Assessment with R and the CITAN Package, Journal of Informetrics 5(4), 2011, pp. 678-692.

Gagolewski M., Grzegorzewski P., A Geometric Approach to the Construction of Scientific Impact Indices, Scientometrics 81(3), 2009, pp. 617-634.

Gagolewski M., Statistical Hypothesis Test for the Difference between Hirsch Indices of Two Pareto-Distributed Random Samples, In: Kruse R. et al (Eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis (AISC 190), Springer-Verlag, Heidelberg, 2013, pp. 359-367.

Gagolewski M., On the Relation Between Effort-Dominating and Symmetric Minitive Aggregation Operators, In: Greco S. et al (Eds.), Advances in Computational Intelligence, Part III (CCIS 299), Springer-Verlag, Heidelberg, 2012, pp. 276-285.

Gagolewski M., Debski M., Nowakiewicz M., Efficient Algorithm for Computing Certain Graph-Based Monotone Integrals: the lp-Indices, In: Mesiar R., Bacigal T. (Eds.), Proc. Uncertainty Modelling, STU Bratislava, ISBN:978-80-227-4067-8, 2013, pp. 17-23.

Gagolewski M., Grzegorzewski P., Axiomatic Characterizations of (quasi-) L-statistics and S-statistics and the Producer Assessment Problem, In: Galichet S., Montero J., Mauris G. (Eds.), Proc. EUSFLAT/LFA 2011, Atlantic Press, 2011, pp. 53-58.

Gagolewski M., Grzegorzewski P., S-Statistics and Their Basic Properties, In: Borgelt C. et al (Eds.), Combining Soft Computing and Statistical Methods in Data Analysis (AISC 77), Springer-Verlag, Heidelberg, 2010, pp. 281-288.

Gagolewski M., Grzegorzewski P., Arity-Monotonic Extended Aggregation Operators, In: Hullermeier E., Kruse R., Hoffmann F. (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems (CCIS 80), Springer-Verlag, Heidelberg, 2010, pp. 693-702.

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[Package agop version 0.2-0 Index]