Function reference
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wrap.correlation() - Prepare Data on Correlation Manifold
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wrap.euclidean() - Prepare Data on Euclidean Space
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wrap.grassmann() - Prepare Data on Grassmann Manifold
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wrap.landmark() - Wrap Landmark Data on Shape Space
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wrap.multinomial() - Prepare Data on Multinomial Manifold
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wrap.rotation() - Prepare Data on Rotation Group
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wrap.spd() - Prepare Data on Symmetric Positive-Definite (SPD) Manifold
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wrap.spdk() - Prepare Data on SPD Manifold of Fixed-Rank
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wrap.sphere() - Prepare Data on Sphere
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wrap.stiefel() - Prepare Data on (Compact) Stiefel Manifold
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riem.interp() - Geodesic Interpolation
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riem.interps() - Geodesic Interpolation of Multiple Points
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riem.pdist() - Compute Pairwise Distances for Data
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riem.pdist2() - Compute Pairwise Distances for Two Sets of Data
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riem.wasserstein() - Wasserstein Distance between Empirical Measures
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predict(<m2skreg>) - Prediction for Manifold-to-Scalar Kernel Regression
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riem.fanova()riem.fanovaP() - Fréchet Analysis of Variance
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riem.m2skreg() - Manifold-to-Scalar Kernel Regression
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riem.m2skregCV() - Manifold-to-Scalar Kernel Regression with K-Fold Cross Validation
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riem.mean() - Fréchet Mean and Variation
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riem.median() - Fréchet Median and Variation
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riem.test2bg14() - Two-Sample Test modified from Biswas and Ghosh (2014)
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riem.test2wass() - Two-Sample Test with Wasserstein Metric
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riem.clrq() - Competitive Learning Riemannian Quantization
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riem.hclust() - Hierarchical Agglomerative Clustering
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riem.kmeans() - K-Means Clustering
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riem.kmeans18B() - K-Means Clustering with Lightweight Coreset
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riem.kmeanspp() - K-Means++ Clustering
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riem.kmedoids() - K-Medoids Clustering
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riem.nmshift() - Nonlinear Mean Shift
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riem.sc05Z() - Spectral Clustering by Zelnik-Manor and Perona (2005)
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riem.scNJW() - Spectral Clustering by Ng, Jordan, and Weiss (2002)
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riem.scSM() - Spectral Clustering by Shi and Malik (2000)
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riem.scUL() - Spectral Clustering with Unnormalized Laplacian
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riem.isomap() - Isometric Feature Mapping
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riem.kpca() - Kernel Principal Component Analysis
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riem.mds() - Multidimensional Scaling
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riem.pga() - Principal Geodesic Analysis
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riem.phate() - PHATE
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riem.sammon() - Sammon Mapping
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riem.tsne() - t-distributed Stochastic Neighbor Embedding
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riem.distlp() - Distance between Two Curves on Manifolds
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riem.dtw() - Dynamic Time Warping Distance
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riem.coreset18B() - Build Lightweight Coreset
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riem.knn() - Find K-Nearest Neighbors
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riem.rmml() - Riemannian Manifold Metric Learning
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riem.seb() - Find the Smallest Enclosing Ball
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moSL()loglkd(<moSL>)label(<moSL>)density(<moSL>) - Finite Mixture of Spherical Laplace Distributions
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moSN()loglkd(<moSN>)label(<moSN>)density(<moSN>) - Finite Mixture of Spherical Normal Distributions
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sphere.geo2xyz()sphere.xyz2geo() - Convert between Cartesian Coordinates and Geographic Coordinates
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sphere.runif() - Generate Uniform Samples on Sphere
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sphere.utest() - Test of Uniformity on Sphere
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stiefel.optSA() - Simulated Annealing on Stiefel Manifold
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stiefel.runif() - Generate Uniform Samples on Stiefel Manifold
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stiefel.utest() - Test of Uniformity on Stiefel Manifold
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grassmann.optmacg() - Estimation of Distribution Algorithm with MACG Distribution
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grassmann.runif() - Generate Uniform Samples on Grassmann Manifold
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grassmann.utest() - Test of Uniformity on Grassmann Manifold
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spd.geometry() - Supported Geometries on SPD Manifold
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spd.pdist() - Pairwise Distance on SPD Manifold
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spd.wassbary() - Wasserstein Barycenter of SPD Matrices
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dmacg()rmacg()mle.macg() - Matrix Angular Central Gaussian Distribution
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dsplaplace()rsplaplace()mle.splaplace() - Spherical Laplace Distribution
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dspnorm()rspnorm()mle.spnorm() - Spherical Normal Distribution