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>.
|Author||Jerome Mariette [aut, cre], Celine Brouard [aut], Remi Flamary [aut], Nathalie Vialaneix [aut]|
|Maintainer||Jerome Mariette <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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