Riemann0.1.6 package

Learning with Data on Riemannian Manifolds

acg

Angular Central Gaussian Distribution

density

S3 method for mixture model : evaluate density

grassmann.optmacg

Estimation of Distribution Algorithm with MACG Distribution

grassmann.runif

Generate Uniform Samples on Grassmann Manifold

grassmann.utest

Test of Uniformity on Grassmann Manifold

label

S3 method for mixture model : predict labels

loglkd

S3 method for mixture model : log-likelihood

macg

Matrix Angular Central Gaussian Distribution

moSL

Finite Mixture of Spherical Laplace Distributions

moSN

Finite Mixture of Spherical Normal Distributions

predict.m2skreg

Prediction for Manifold-to-Scalar Kernel Regression

riem.clrq

Competitive Learning Riemannian Quantization

riem.coreset18B

Build Lightweight Coreset

riem.distlp

Distance between Two Curves on Manifolds

riem.dtw

Dynamic Time Warping Distance

riem.fanova

Fréchet Analysis of Variance

riem.hclust

Hierarchical Agglomerative Clustering

riem.interp

Geodesic Interpolation

riem.interps

Geodesic Interpolation of Multiple Points

riem.isomap

Isometric Feature Mapping

riem.kmeans

K-Means Clustering

riem.kmeans18B

K-Means Clustering with Lightweight Coreset

riem.kmeanspp

K-Means++ Clustering

riem.kmedoids

K-Medoids Clustering

riem.knn

Find K-Nearest Neighbors

riem.kpca

Kernel Principal Component Analysis

riem.m2skreg

Manifold-to-Scalar Kernel Regression

riem.m2skregCV

Manifold-to-Scalar Kernel Regression with K-Fold Cross Validation

riem.mds

Multidimensional Scaling

riem.mean

Fréchet Mean and Variation

riem.median

Fréchet Median and Variation

riem.nmshift

Nonlinear Mean Shift

riem.pdist

Compute Pairwise Distances for Data

riem.pdist2

Compute Pairwise Distances for Two Sets of Data

riem.pga

Principal Geodesic Analysis

riem.phate

PHATE

riem.rmml

Riemannian Manifold Metric Learning

riem.sammon

Sammon Mapping

riem.sc05Z

Spectral Clustering by Zelnik-Manor and Perona (2005)

riem.scNJW

Spectral Clustering by Ng, Jordan, and Weiss (2002)

riem.scSM

Spectral Clustering by Shi and Malik (2000)

riem.scUL

Spectral Clustering with Unnormalized Laplacian

riem.seb

Find the Smallest Enclosing Ball

riem.test2bg14

Two-Sample Test modified from Biswas and Ghosh (2014)

riem.test2wass

Two-Sample Test with Wasserstein Metric

riem.tsne

t-distributed Stochastic Neighbor Embedding

riem.wasserstein

Wasserstein Distance between Empirical Measures

rmvnorm

Generate Random Samples from Multivariate Normal Distribution

spd.geometry

Supported Geometries on SPD Manifold

spd.pdist

Pairwise Distance on SPD Manifold

spd.wassbary

Wasserstein Barycenter of SPD Matrices

sphere.convert

Convert between Cartesian Coordinates and Geographic Coordinates

sphere.runif

Generate Uniform Samples on Sphere

sphere.utest

Test of Uniformity on Sphere

splaplace

Spherical Laplace Distribution

spnorm

Spherical Normal Distribution

stiefel.optSA

Simulated Annealing on Stiefel Manifold

stiefel.runif

Generate Uniform Samples on Stiefel Manifold

stiefel.utest

Test of Uniformity on Stiefel Manifold

wrap.correlation

Prepare Data on Correlation Manifold

wrap.euclidean

Prepare Data on Euclidean Space

wrap.grassmann

Prepare Data on Grassmann Manifold

wrap.landmark

Wrap Landmark Data on Shape Space

wrap.multinomial

Prepare Data on Multinomial Manifold

wrap.rotation

Prepare Data on Rotation Group

wrap.spd

Prepare Data on Symmetric Positive-Definite (SPD) Manifold

wrap.spdk

Prepare Data on SPD Manifold of Fixed-Rank

wrap.sphere

Prepare Data on Sphere

wrap.stiefel

Prepare Data on (Compact) Stiefel Manifold

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.

  • Maintainer: Kisung You
  • License: MIT + file LICENSE
  • Last published: 2025-09-26