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
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:

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