Provides tools for exploiting topological information into standard statistical learning algorithms. To this aim, this package contains the most popular kernels defined on the space of persistence diagrams, and persistence images. Moreover, it provides a solver for kernel Support Vector Machines problems, whose kernels are not necessarily positive semidefinite, based on the C++ library 'LIBSVM' <https://www.csie.ntu.edu.tw/~cjlin/libsvm/>. Additionally, it allows to compute Wasserstein distance between persistence diagrams with an arbitrary ground metric, building an R interface for the C++ library 'HERA' <https://bitbucket.org/grey_narn/hera/src/master/>.
| Version: | 1.0.0 |
| Imports: | Rcpp (≥ 1.0.1), mvtnorm, Rdpack, methods, stats |
| LinkingTo: | Rcpp, RcppEigen, BH |
| Suggests: | TDA, knitr, rmarkdown, SparseM, Matrix, kernlab, viridis |
| Published: | 2020-04-17 |
| Author: | Tullia Padellini [aut, cre], Francesco Palini [aut], Pierpaolo Brutti [ctb], Chih-Chung Chang [ctb, cph] (LIBSVM C++ code), Chih-Chen Lin [ctb, cph] (LIBSVM C++ code), Michael Kerber [ctb, cph] (HERA C++ code), Dmitriy Morozov [ctb, cph] (HERA C++ code), Arnur Nigmetov [ctb, cph] (HERA C++ code) |
| Maintainer: | Tullia Padellini <t.padellini at imperial.ac.uk> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README NEWS |
| CRAN checks: | kernelTDA results |
| Reference manual: | kernelTDA.pdf |
| Package source: | kernelTDA_1.0.0.tar.gz |
| Windows binaries: | r-devel: kernelTDA_1.0.0.zip, r-release: kernelTDA_1.0.0.zip, r-oldrel: kernelTDA_1.0.0.zip |
| macOS binaries: | r-release: kernelTDA_1.0.0.tgz, r-oldrel: kernelTDA_1.0.0.tgz |
| Old sources: | kernelTDA archive |
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