https://github.com/kisungyou/riemann
Learning with Data on Riemannian Manifolds
https://github.com/kisungyou/riemann
Last synced: 2 months ago
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Learning with Data on Riemannian Manifolds
- Host: GitHub
- URL: https://github.com/kisungyou/riemann
- Owner: kisungyou
- License: other
- Created: 2020-09-05T16:59:33.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T19:44:39.000Z (over 2 years ago)
- Last Synced: 2025-04-09T12:22:23.568Z (3 months ago)
- Language: R
- Homepage: https://kisungyou.com/Riemann
- Size: 222 MB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```# Riemann
[](https://CRAN.R-project.org/package=Riemann)
[](https://github.com/kisungyou/Riemann/actions)
[](https://app.codecov.io/gh/kisungyou/Riemann?branch=master)**Riemann** is an R package for learning with data on **Riemannian manifolds**. In statistics and machine learning, the term *manifold* appears in two realms; one is *dimensionality reduction* where we assume that low-dimensional data manifold is embedded in high-dimensional Euclidean space. The other is *statistics on manifolds* - data lie on some Riemannian manifolds that we are already well aware of. **Riemann** aims to achieve the latter. If you are interested in dimension reduction, please check another R package [Rdimtools](https://kisungyou.com/Rdimtools/).
### Installation
* Option 1 : **released** version from [CRAN](https://CRAN.R-project.org).
``` r
install.packages("Riemann")
```* Option 2 : **development** version from [GitHub](https://github.com/).
``` r
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("kisungyou/Riemann")
```