transport

R package transport: Optimal Transport in Various Forms. Solve optimal transport problems in statistics. Compute Wasserstein metrics (a.k.a. Kantorovitch, Fortet–Mourier, Mallows, Earth Mover’s, or minimal L_p metrics), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, point patterns, and mass vectors.

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References in zbMATH (referenced in 7 articles )

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  1. Ahfock, Daniel; Pyne, Saumyadipta; McLachlan, Geoffrey J.: Statistical file-matching of non-Gaussian data: a game theoretic approach (2022)
  2. Hallin, Marc; Mordant, Gilles; Segers, Johan: Multivariate goodness-of-fit tests based on Wasserstein distance (2021)
  3. Bachoc, François; Suvorikova, Alexandra; Ginsbourger, David; Loubes, Jean-Michel; Spokoiny, Vladimir: Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding (2020)
  4. Gassiat, Elisabeth; Le Corff, Sylvain; Lehéricy, Luc: Identifiability and consistent estimation of nonparametric translation hidden Markov models with general state space (2020)
  5. Hartmann, Valentin; Schuhmacher, Dominic: Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case (2020)
  6. Janßen, Anja; Wan, Phyllis: (k)-means clustering of extremes (2020)
  7. Sommerfeld, Max; Schrieber, Jörn; Zemel, Yoav; Munk, Axel: Optimal transport: fast probabilistic approximation with exact solvers (2019)