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>.
| Version: | 0.3 |
| Depends: | R (≥ 2.10), mixOmics, ggplot2 |
| Imports: | vegan, phyloseq, corrplot, psych, quadprog, LDRTools, Matrix, methods |
| Published: | 2018-11-26 |
| Author: | Jerome Mariette [aut, cre], Nathalie Villa-Vialaneix [aut] |
| 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_0.3.tar.gz |
| Windows binaries: | r-devel: mixKernel_0.3.zip, r-release: mixKernel_0.3.zip, r-oldrel: mixKernel_0.3.zip |
| OS X binaries: | r-release: mixKernel_0.3.tgz, r-oldrel: mixKernel_0.3.tgz |
| Old sources: | mixKernel archive |
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