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.
| Version: | 0.1 |
| Depends: | R (≥ 2.10), mixOmics, ggplot2 |
| Imports: | phyloseq, corrplot, psych, quadprog, LDRTools |
| Published: | 2017-05-18 |
| Author: | 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")) |
| Maintainer: | Jerome Mariette <jerome.mariette at inra.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | mixKernel citation info |
| Materials: | NEWS |
| CRAN checks: | mixKernel results |
| Reference manual: | mixKernel.pdf |
| Package source: | mixKernel_0.1.tar.gz |
| Windows binaries: | r-devel: mixKernel_0.1.zip, r-release: mixKernel_0.1.zip, r-oldrel: mixKernel_0.1.zip |
| OS X El Capitan binaries: | r-release: not available |
| OS X Mavericks binaries: | r-oldrel: mixKernel_0.1.tgz |
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