Package: mixKernel
Type: Package
Title: Omics Data Integration Using Kernel Methods
Version: 0.5
Date: 2021-03-01
Depends: R (>= 3.5.0), mixOmics, ggplot2, reticulate (>= 1.14)
Imports: vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix,
        methods
Authors@R: c(person("Jerome", "Mariette", role = c("aut", "cre"), email="jerome.mariette@inrae.fr"),
             person("Celine", "Brouard", role = c("aut"), email="celine.brouard@inrae.fr"),
             person("Remi", "Flamary", role = c("aut"), email="remi.flamary@polytechnique.edu"),
             person("Nathalie", "Vialaneix", role = c("aut"), email="nathalie.vialaneix@inrae.fr"))
Maintainer: Jerome Mariette <jerome.mariette@inrae.fr>
Author: Jerome Mariette [aut, cre],
  Celine Brouard [aut],
  Remi Flamary [aut],
  Nathalie Vialaneix [aut]
Description: Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view. Functions to assess and display
    important variables are also provided in the package. Jerome Mariette and 
    Nathalie Villa-Vialaneix (2018) <doi:10.1093/bioinformatics/btx682>.
License: GPL (>= 2)
Repository: CRAN
Date/Publication: 2021-03-30 18:20:03 UTC
Packaged: 2021-03-01 08:00:43 UTC; jmariette
NeedsCompilation: no
LazyData: true
Config/reticulate: list( packages = list( list(package = "autograd",
        pip = TRUE), list(package = "numpy", pip = TRUE), list(package
        = "scipy", pip = TRUE), list(package = "sklearn", pip = TRUE) )
        )
Built: R 4.0.3; ; 2021-03-30 20:26:10 UTC; unix
