Matrix, Rcpp (>= 0.12.12), bit, ff, ffbase, methods, survival,
MASS

rlang, Matrix, Rcpp (>= 0.12.12), Andromeda (>= 0.3.1), dplyr,
dbplyr, methods, survival
testthat, MASS, gnm, ggplot2, microbenchmark

c(
person("Marc A.", "Suchard", email = "msuchard@ucla.edu", role = c("aut","cre")),
person("Martijn J.", "Schuemie", role = "aut"),
person("Trevor R.", "Shaddox", role = "aut"),
person("Yuxi", "Tian", role = "aut"),
person("Jianxiao", "Yang", role = "aut"),
person("Eric", "Kawaguchi", role = "aut"),
person("Sushil", "Mittal", role ="ctb"),
person("Observational Health Data Sciences and Informatics", role = "cph"),
person("Marcus", "Geelnard", role = c("cph", "ctb"),
comment = "provided the TinyThread library"),
person("Rutgers University", role = c("cph", "ctb"),
comment = "provided the HParSearch routine"),
person("R Development Core Team", role = c("cph", "ctb"),
comment = "provided the ZeroIn routine")
)
rlang, Matrix, Rcpp (>= 0.12.12), Andromeda (>= 0.3.1), dplyr,
methods, survival, bit64
testthat, MASS, gnm, ggplot2, microbenchmark, cmprsk
Marc A. Suchard [aut, cre],
Martijn J. Schuemie [aut],
Trevor R. Shaddox [aut],
Yuxi Tian [aut],
Jianxiao Yang [aut],
Eric Kawaguchi [aut],
Sushil Mittal [ctb],
Observational Health Data Sciences and Informatics [cph],
Marcus Geelnard [cph, ctb] (provided the TinyThread library),
Rutgers University [cph, ctb] (provided the HParSearch routine),
R Development Core Team [cph, ctb] (provided the ZeroIn routine)

testthat, readr, MASS, gnm, ggplot2, microbenchmark, cmprsk

Optimal Transport for Gating Transfer in Cytometry Data with
Domain Adaptation
c(person("Boris", "Hejblum", role = c("aut", "cre"), email = "boris.hejblum@u-bordeaux.fr"),
person("Paul", "Freulon", role = c("aut"), email = "paul.freulon@math.u-bordeaux.fr"),
person("Kalidou", "Ba", role = c("aut", "trl")))
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 (2021) <arXiv:2006.09003>.
list( packages = list( list(package = "numpy"),
list(package = "scikit-learn"), list(package = "scipy") ) )
ggplot2 (>= 3.0.0), MetBrewer, patchwork, reshape2,
reticulate, stats, testthat (>= 3.0.0)
Boris Hejblum [aut, cre],
Paul Freulon [aut],
Kalidou Ba [aut, trl]