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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 ORCID iD [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:

Reference manual: Riemann.pdf
Vignettes: Riemann 101 : A First Step

Downloads:

Package source: Riemann_0.1.4.tar.gz
Windows binaries: r-devel: Riemann_0.1.4.zip, r-release: Riemann_0.1.4.zip, r-oldrel: Riemann_0.1.4.zip
macOS binaries: r-release (arm64): Riemann_0.1.4.tgz, r-oldrel (arm64): Riemann_0.1.4.tgz, r-release (x86_64): Riemann_0.1.4.tgz, r-oldrel (x86_64): Riemann_0.1.4.tgz
Old sources: Riemann archive

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.