vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix,
methods
Jerome Mariette [aut, cre],
Nathalie Villa-Vialaneix [aut]

c(
person("Jerome", "Mariette", role = c("aut", "cre"), email="jerome.mariette@inra.fr"),
person("Nathalie", "Villa-Vialaneix", role = c("aut"), email="nathalie.villa-vialaneix@inra.fr"))
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 (2017) <doi:10.1093/bioinformatics/btx682>.

R (>= 3.5.0), mixOmics, ggplot2, reticulate (>= 1.14)
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@unice.fr"),
person("Nathalie", "Vialaneix", role = c("aut"), email="nathalie.vialaneix@inrae.fr"))
list( packages = list( list(package = "autograd",
pip = TRUE), list(package = "numpy", pip = TRUE), list(package
= "scipy", pip = TRUE) ) )
Jerome Mariette [aut, cre],
Celine Brouard [aut],
Remi Flamary [aut],
Nathalie Vialaneix [aut]

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"))
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>.
list( packages = list( list(package = "autograd",
pip = TRUE), list(package = "numpy", pip = TRUE), list(package
= "scipy", pip = TRUE), list(package = "sklearn", pip = TRUE) )
)