Package: riemannianStats 0.2.0

Oldemar Rodríguez Rojas

riemannianStats: Riemannian Methods for Principal Component Analysis, Regression and Visualization

Provides tools for statistical analysis on Riemannian manifolds using local geometry derived from Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The package supports dimensionality reduction, visualization, Riemannian principal component analysis, and Riemannian linear regression for multivariate data analysis. Methods based on Uniform Manifold Approximation and Projection follow McInnes et al. (2018) <doi:10.21105/joss.00861>.

Authors:Oldemar Rodríguez Rojas [aut, cre], Jennifer Lobo Vásquez [aut]

riemannianStats_0.2.0.tar.gz
riemannianStats_0.2.0.zip(r-4.7-any)riemannianStats_0.2.0.zip(r-4.6-any)
riemannianStats_0.2.0.tar.gz(r-4.7-any)riemannianStats_0.2.0.tar.gz(r-4.6-any)
riemannianStats_0.2.0.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'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.98 score 12 scripts 283 downloads 20 exports 39 dependencies

Last updated from:8a107f8264. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK175
source / vignettesOK249
linux-releaseOK172
windows-develOK106
windows-releaseOK113
wasm-releaseOK119

Exports:riem.biplotriem.center.datariem.corriem.covriem.diffriem.distriem.ind.coordriem.inertiariem.lmriem.mean.indexriem.normriem.original.scaleriem.pcariem.plotriem.plot3driem.rhoriem.similarities.dbscanriem.similarities.isomapriem.similarities.umapriem.var.coord

Dependencies:BHcliclustercpp11dbscandqrngfarverFNNgenericsggplot2ggrepelgluegtableirlbaisobandlabelinglatticelifecycleMASSMatrixmgcvnlmepermuteR6RColorBrewerRcppRcppAnnoyRcppEigenRcppProgressrlangRSpectraS7scalessitmouwotvctrsveganviridisLitewithr

Classical and Riemannian Linear Regression with UMAP, ISOMAP, and DBSCAN
Overview | 1. Load the package | 2. Load the data set | 3. Fit a classical linear regression model | 4. Fit a Riemannian linear regression model using a formula | Formula specification options | Use one explanatory variable | List the explanatory variables explicitly | Use all the remaining variables | Exclude a variable | Include an interaction | Include a mathematical transformation | Include categorical variables | 5. Fit the model by passing the variables separately | 6. Inspect the Riemannian regression results with UMAP | 7. Convert UMAP Riemannian results to the original scale | 8. Fit a Riemannian linear regression model using ISOMAP similarities | 9. Inspect the Riemannian regression results with ISOMAP | 10. Convert ISOMAP Riemannian results to the original scale | 11. Fit a Riemannian linear regression model using DBSCAN similarities | 12. Inspect the Riemannian regression results with DBSCAN | 13. Convert DBSCAN Riemannian results to the original scale | 14. Compare the models | 15. Summary
Rendered fromriemannian-regression-umap-dbscan-isomap.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Complete Riemannian PCA Workflow with riem.pca
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
Rendered fromstudent-pca-umap-complete-workflow.Rmd.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Step-by-Step Riemannian PCA with UMAP on Synthetic Data
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
Rendered fromdata10d250-pca-umap-step-by-step.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Simple Example of Riemannian STATS: Student DataSet - ACP-UMAP
Overview | 1. Load the package | 2. Create the student data set | 3. Prepare the data for the analysis | 4. Fit the Riemannian PCA model | 5. Inspect the eigenvalues | 6. Inspect the UMAP similarity matrix | 7. Inspect the Rho matrix | 8. Inspect the Riemannian differences | 9. Inspect the Riemannian distance matrix | 10. Inspect the Riemannian covariance matrix | 11. Inspect the Riemannian correlation matrix | 12. Inspect the individual coordinates | 13. Inspect the variable coordinates | 14. Plot individuals using the object interface | 15. Plot variables using the object interface | 16. Create a Riemannian biplot using the object interface | 17. Plot individuals using the manual interface | 18. Plot variables using the manual interface | 19. Summary
Rendered fromstudent-pca-umap-step-by-step.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Readme and manuals

Help Manual

Help pageTopics
Extract Riemannian Regression Coefficientscoef.riem.lm
Extract Riemannian Fitted Valuesfitted.riem.lm
Print a Riemannian Linear Regression Modelprint.riem.lm
Print method for Riemannian PCAprint.riem.pca
Print a Summary of a Riemannian Linear Regression Modelprint.summary.riem.lm
Extract Riemannian Residualsresiduals.riem.lm
Riemannian PCA Biplotriem.biplot
Default Method for Riemannian PCA Biplotsriem.biplot.default
Riemannian PCA Biplotriem.biplot.riem.pca
Center Data Around the Riemannian Mean Observationriem.center.data
Calculate the Riemannian Correlation Matrixriem.cor
Calculate a Riemannian Covariance Matrixriem.cov
Calculate Riemannian Vector Differencesriem.diff
Calculate the Distance Matrixriem.dist
Calculate Riemannian Principal Componentsriem.ind.coord
Calculate PCA Inertia for Two Componentsriem.inertia
Riemannian Linear Regressionriem.lm
Find the Riemannian Mean Observation Indexriem.mean.index
Calculate the Euclidean Normriem.norm
Convert a Riemannian Regression Fit to the Original Scaleriem.original.scale
Riemannian Principal Component Analysis Riemannian Principal Component Analysisriem.pca
Plot Riemannian PCA Resultsriem.plot
Default Method for Riemannian PCA Plotsriem.plot.default
Plot Riemannian PCA Resultsriem.plot.riem.pca
Plot a Three-Dimensional Scatter Plotriem.plot3d
Calculate the Rho Matrixriem.rho
Calculate DBSCAN-Induced Similaritiesriem.similarities.dbscan
Calculate Isomap Graph Similaritiesriem.similarities.isomap
Calculate UMAP Graph Similaritiesriem.similarities.umap
Calculate Riemannian Correlations Between Variables and Componentsriem.var.coord
Summarize a Riemannian Linear Regression Modelsummary.riem.lm