| acg | Angular Central Gaussian Distribution | 
| cities | Data : Populated Cities in the U.S. | 
| dacg | Angular Central Gaussian Distribution | 
| density | S3 method for mixture model : evaluate density | 
| density.moSL | Finite Mixture of Spherical Laplace Distributions | 
| density.moSN | Finite Mixture of Spherical Normal Distributions | 
| dmacg | Matrix Angular Central Gaussian Distribution | 
| dsplaplace | Spherical Laplace Distribution | 
| dspnorm | Spherical Normal Distribution | 
| ERP | Data : EEG Covariances for Event-Related Potentials | 
| gorilla | Data : Gorilla Skull | 
| 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 | 
| hands | Data : Left Hands | 
| label | S3 method for mixture model : predict labels | 
| label.moSL | Finite Mixture of Spherical Laplace Distributions | 
| label.moSN | Finite Mixture of Spherical Normal Distributions | 
| loglkd | S3 method for mixture model : log-likelihood | 
| loglkd.moSL | Finite Mixture of Spherical Laplace Distributions | 
| loglkd.moSN | Finite Mixture of Spherical Normal Distributions | 
| macg | Matrix Angular Central Gaussian Distribution | 
| mle.acg | Angular Central Gaussian Distribution | 
| mle.macg | Matrix Angular Central Gaussian Distribution | 
| mle.splaplace | Spherical Laplace Distribution | 
| mle.spnorm | Spherical Normal Distribution | 
| moSL | Finite Mixture of Spherical Laplace Distributions | 
| moSN | Finite Mixture of Spherical Normal Distributions | 
| orbital | Data : Normal Vectors to the Orbital Planes of the 9 Planets | 
| passiflora | Data : Passiflora Leaves | 
| predict.m2skreg | Prediction for Manifold-to-Scalar Kernel Regression | 
| racg | Angular Central Gaussian Distribution | 
| 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.fanovaP | 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 | 
| rmacg | Matrix Angular Central Gaussian Distribution | 
| rmvnorm | Generate Random Samples from Multivariate Normal Distribution | 
| rsplaplace | Spherical Laplace Distribution | 
| rspnorm | Spherical 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.geo2xyz | Convert between Cartesian Coordinates and Geographic Coordinates | 
| sphere.runif | Generate Uniform Samples on Sphere | 
| sphere.utest | Test of Uniformity on Sphere | 
| sphere.xyz2geo | Convert between Cartesian Coordinates and Geographic Coordinates | 
| 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 |