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DESCRIPTION
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DESCRIPTION
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Package: mixKernel
Type: Package
Title: Omics Data Integration Using Kernel Methods
Version: 0.9-1
Date: 2024-01-27
Depends: R (>= 3.5.0), mixOmics, ggplot2, reticulate (>= 1.14)
Imports: vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix,
methods, markdown
Suggests: rmarkdown, knitr
Authors@R: c(person("Nathalie", "Vialaneix", role = c("aut", "cre"), email="[email protected]"),
person("Celine", "Brouard", role = c("aut"), email="[email protected]"),
person("Remi", "Flamary", role = c("aut"), email="[email protected]"),
person("Julien", "Henry", role = c("aut"), email="[email protected]"),
person("Jerome", "Mariette", role = c("aut"), email="[email protected]"))
Maintainer: Nathalie Vialaneix <[email protected]>
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
<doi:10.1093/bioinformatics/btx682>. A method to select (as well as
funtions to display) important variables is also provided
<doi:10.1093/nargab/lqac014>.
License: GPL (>= 2)
Repository: CRAN
BugReports: https://forgemia.inra.fr/genotoul-bioinfo/mixKernel/-/issues
VignetteBuilder: knitr
Encoding: UTF-8
NeedsCompilation: no
URL: http://mixkernel.clementine.wf
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) )
)
RoxygenNote: 7.3.0
Packaged: 2024-01-27 13:58:14 UTC; nathalie
Author: Nathalie Vialaneix [aut, cre],
Celine Brouard [aut],
Remi Flamary [aut],
Julien Henry [aut],
Jerome Mariette [aut]
Date/Publication: 2024-01-27 14:20:02 UTC