# RiemGrassmann v0.1.0

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## Inference, Learning, and Optimization on Grassmann Manifold

Grassmann manifold is a set of k-planes or linear subspaces in Euclidean space. We provide algorithms for statistical inference, optimization, and learning over the Grassmann manifold. For general exposition to the statistics on the manifold, see the book by Chikuse (2003) <doi:10.1007/978-0-387-21540-2>.

# Inference, Learning, and Optimization on Grassmann manifold

Grassmannian is a set of linear subspaces, which forms a Riemannian manifold. We provide algorithms for statistical inference, optimization, and learning over the Grassmann manifold.

## Installation

You can install the released version of RiemGrassmann from CRAN with:

install.packages("RiemGrassmann")


And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("kyoustat/RiemGrassmann")


## Available Functions

function description
gr.hclust Hierarchical clustering.
gr.kmedoids k-Medoids clustering.
gr.mean Frechet mean and variation.
gr.pdist Pairwise distance for Grassmann-valued data
gr.pdist2 Pairwise distance between two sets of data

## Functions in RiemGrassmann

 Name Description package-RiemGrassmann Inference, Learning, and Optimization on Grassmann Manifold gr.pdist2 Pairwise Distance for Two Sets Data on Grassmann Manifold gr.kmedoids k-Medoids Clustering on Grassmann Manifold gr.pdist Pairwise Distance for Data on Grassmann Manifold gr.mean Frchet Mean on Grassmann Manifold gr.hclust Hierarchical Agglomerative Clustering on Grassmann Manifold No Results!