Package: CvM2SL2Test
Version: 0.1-1
Date: 2010-11-30
Title: Cramer-von Mises Two Sample Tests
Author: Yuanhui Xiao <matyxx@langate.gsu.edu> and Ye Cui
Maintainer: Yuanhui Xiao <matyxx@langate.gsu.edu>
Depends:
Suggests:
Description: The package contains two functions: one computes the
Cramer-von Mises two sample test scores, the other cmoputes the
exact p-value(s) for given Cramer-von Mises two-sample test
score(s) under the assumption that the populations under
comparison have the same probability distribution. Like
Kolmogorov-Smirnov two-sample test, Cramer-von Mises test is
also distribution free. In certain cases, the Cramer-von Mises
test is more powerful than the Kolmogorov-Smirnov test, but it
is less widely used than the latter.
License: GPL (>= 2)
Packaged: 2010-12-01 03:23:18 UTC; YuanhuiXiao
Repository: CRAN
Date/Publication: 2010-12-01 08:01:37

Packaged: 2012-01-02 22:00:03 UTC; YuanhuiXiao

Title: Cyclic Coordinate Descent for Logistic, Poisson and Survival
Description: This model fitting tool incorporates cyclic coordinate descent and
majorization-minimization approaches to fit a variety of regression models
found in large-scale observational healthcare data.  Implementations focus
on computational optimization and fine-scale parallelization to yield
efficient inference in massive datasets.
Imports: Matrix, Rcpp (>= 0.12.4), bit, ff, ffbase, RcppParallel,
LinkingTo: Rcpp, BH (>= 1.51.0), RcppEigen (>= 0.3.2), RcppParallel
Suggests: testthat, survival, gnm, ggplot2

Title: Optimal Transport for Gating Transfer in Cytometry Data with
Authors@R: 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")))
Description: 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>.
Config/reticulate: list( packages = list( list(package = "numpy"),
list(package = "scikit-learn"), list(package = "scipy") ) )
Imports: ggplot2 (>= 3.0.0), MetBrewer, patchwork, reshape2,