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.
|Author||c(person("Jerome", "Mariette", role = c("aut", "cre"), email="[email protected]"), person("Nathalie", "Villa-Vialaneix", role = c("aut"), email="[email protected]"))|
|Date of publication||2017-05-18 03:50:57 UTC|
|Maintainer||Jerome Mariette <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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