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https://github.com/kisungyou/riemann

Learning with Data on Riemannian Manifolds
https://github.com/kisungyou/riemann

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Learning with Data on Riemannian Manifolds

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README

        

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```

# Riemann

[![CRAN status](https://www.r-pkg.org/badges/version/Riemann)](https://CRAN.R-project.org/package=Riemann)
[![R-CMD-check](https://github.com/kisungyou/Riemann/workflows/R-CMD-check/badge.svg)](https://github.com/kisungyou/Riemann/actions)
[![Codecov test coverage](https://codecov.io/gh/kisungyou/Riemann/branch/master/graph/badge.svg)](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")
```