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:
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