Overview | 1. Load the data set | 2. Prepare the data | 3. Choose the number of neighbors | 4. Compute the UMAP similarities | 5. Compute the Rho matrix | 6. Compute the Riemannian vector differences | 7. Compute the UMAP-based Riemannian distance matrix | 8. Riemannian correlation matrix | 10. Riemannian covariance matrix | 10. Riemannian principal components | 11. Explained inertia | 12. Correlations between variables and components | Estan mal los resultado******************* Dan mal los negativos | 13. Visualizations | 13.1 Principal plane | 13.2 Correlation circle | 13.3 Biplot | 13.4 3D plot
Package: riemannianStats 0.1.1
Oldemar Rodríguez Rojas
riemannianStats: Riemannian Statistics for Dimensionality Reduction and Visualization
Provides tools for applying statistical methods on Riemannian manifolds using local geometry derived from Uniform Manifold Approximation and Projection (UMAP). The package enables dimensionality reduction, visualization, and analysis of complex data through Riemannian versions of principal component analysis and related multivariate methods. Methods are based on McInnes et al. (2018) <doi:10.21105/joss.00861>.
Authors:
riemannianStats_0.1.1.tar.gz
riemannianStats_0.1.1.tar.gz(r-4.7-any)riemannianStats_0.1.1.tar.gz(r-4.6-any)
riemannianStats_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
riemannianStats/json (API)
| # Install 'riemannianStats' in R: |
| install.packages('riemannianStats', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:6d07b90afe. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 165 | ||
| source / vignettes | OK | 248 | ||
| linux-release-x86_64 | OK | 161 | ||
| wasm-release | OK | 157 |
Exports:riem.biplotriem.center.datariem.corriem.covriem.diffriem.distriem.ind.coordriem.inertiariem.mean.indexriem.normriem.plotriem.plot.3driem.rhoriem.similarities.umapriem.var.coord
Dependencies:BHclicpp11dqrngfarverFNNggplot2ggrepelgluegtableirlbaisobandlabelinglatticelifecycleMatrixR6RColorBrewerRcppRcppAnnoyRcppEigenRcppProgressrlangRSpectraS7scalessitmouwotvctrsviridisLitewithr
Riemannian STATS Example: Data10D_250 Data Set
Last update: 2026-07-17
Started: 2026-07-17
Simple Example of Riemannian STATS: Student Data Set
Overview | 1. Create the data set | 2. Prepare the data for the analysis | 3. Compute UMAP similarities | 4. Compute the Rho matrix | 5. Compute Riemannian differences and distances | 6. Compute Riemannian covariance and correlation | 7. Compute Riemannian principal components | 8. Compute explained inertia | 9. Interpret the subjects in the reduced space | 10. Visualize the principal plane | 11. Visualize the correlation circle | 12. Visualize the biplot
Last update: 2026-07-17
Started: 2026-07-17
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plot a Riemannian Biplot | riem.biplot |
| Center Data Around the Riemannian Mean Observation | riem.center.data |
| Calculate the Riemannian Correlation Matrix | riem.cor |
| Calculate a Riemannian Covariance Matrix | riem.cov |
| Calculate Riemannian Vector Differences | riem.diff |
| Calculate the UMAP-Based Distance Matrix | riem.dist |
| Calculate Riemannian Principal Components | riem.ind.coord |
| Calculate PCA Inertia for Two Components | riem.inertia |
| Find the Riemannian Mean Observation Index | riem.mean.index |
| Calculate the Euclidean Norm | riem.norm |
| Plot Riemannian Results | riem.plot |
| Plot a Three-Dimensional Scatter Plot | riem.plot.3d |
| Calculate the Rho Matrix | riem.rho |
| Calculate UMAP Graph Similarities | riem.similarities.umap |
| Calculate Riemannian Correlations Between Variables and Components | riem.var.coord |