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Riemann: Learning with Data on Riemannian Manifolds
We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.
| Version: |
0.1.4 |
| Depends: |
R (≥ 2.10) |
| Imports: |
CVXR, Rcpp (≥ 1.0.5), Rdpack, RiemBase, Rdimtools, T4cluster, DEoptim, lpSolve, Matrix, maotai (≥ 0.2.2), stats, utils |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
R.rsp, knitr, rmarkdown |
| Published: |
2022-02-28 |
| DOI: |
10.32614/CRAN.package.Riemann |
| Author: |
Kisung You  [aut,
cre] |
| Maintainer: |
Kisung You <kisungyou at outlook.com> |
| BugReports: |
https://github.com/kisungyou/Riemann/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://kisungyou.com/Riemann/ |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
Riemann results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=Riemann
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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