PerMallows

R package PerMallows: Permutations and Mallows Distributions. Includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall’s-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall’s-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence).


References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Gilbert, Hugo; Portoleau, Tom; Spanjaard, Olivier: Beyond pairwise comparisons in social choice: a setwise Kemeny aggregation problem (2022)
  2. Chen, Yilin; DeJong, Jennifer; Halverson, Tom; Shuman, David I.: Signal processing on the permutahedron: tight spectral frames for ranked data analysis (2021)
  3. Mollica, Cristina; Tardella, Luca: PLMIX: an R package for modelling and clustering partially ranked data (2020)
  4. Turner, Heather L.; van Etten, Jacob; Firth, David; Kosmidis, Ioannis: Modelling rankings in (\mathsfR): the \textbfPlackettLucepackage (2020)
  5. Irurozki, Ekhine; Calvo, Borja; Lozano, Jose A.: Mallows and generalized Mallows model for matchings (2019)
  6. Lomelí, M.; Rowland, M.; Gretton, A.; Ghahramani, Z.: Antithetic and Monte Carlo kernel estimators for partial rankings (2019)
  7. Nikolov, Nikolay I.; Stoimenova, Eugenia: Asymptotic properties of Lee distance (2019)
  8. Yu, Philip L. H.; Xu, Hang: Rank aggregation using latent-scale distance-based models (2019)
  9. Zhaozhi Qian; Philip Yu: Weighted Distance-Based Models for Ranking Data Using the R Package rankdist (2019) not zbMATH
  10. Irurozki, Ekhine; Calvo, Borja; Lozano, Jose A.: Sampling and learning Mallows and generalized Mallows models under the Cayley distance (2018)
  11. Irurozki, Ekhine; Ceberio, Josu; Santamaria, Josean; Santana, Roberto; Mendiburu, Alexander: Algorithm 989: \textttperm_mateda: a Matlab toolbox of estimation of distribution algorithms for permutation-based combinatorial optimization problems (2018)
  12. Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi, Arnoldo; Arjas, Elja: Probabilistic preference learning with the Mallows rank model (2018)
  13. Cristina Mollica, Luca Tardella: PLMIX: An R package for modeling and clustering partially ranked data (2016) arXiv
  14. Ekhine Irurozki and Borja Calvo and Jose Lozano: PerMallows: An R Package for Mallows and Generalized Mallows Models (2016) not zbMATH
  15. Mukherjee, Sumit: Estimation in exponential families on permutations (2016)
  16. Ceberio, Josu; Irurozki, Ekhine; Mendiburu, Alexander; Lozano, Jose A.: A review of distances for the Mallows and generalized Mallows estimation of distribution algorithms (2015)