Optimal Transport for Gating Transfer in Cytometry Data with Domain Adaptation R package CytOpT version 0.9.8 Supervised learning from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. It relies regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. It is based on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible mis-alignment of a given cell population across sample (due to technical variability from the technology of measurements). Supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model Details are presented in Freulon P, Bigot J and Hejblum BP (2023) <doi:10.1214/22-AOAS1660>. Software Depends: R (>= 3.6) Imports: ggplot2 (>= 3.0.0), MetBrewer, patchwork, reshape2, reticulate, stats, testthat (>= 3.0.0) Suggests: rmarkdown, knitr, covr Boris Hejblum <boris.hejblum@u-bordeaux.fr> Comprehensive R Archive Network (CRAN) Boris Hejblum [aut, cre], Paul Freulon [aut], Kalidou Ba [aut, trl] GPL (>= 2) 2025-04-01 application/tgz https://CRAN.R-project.org/package=CytOpT doi:10.32614/CRAN.package.CytOpT en-US