Riemannian Methods for Principal Component Analysis, Regression and Visualization
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK7 OK2026-07-18
Documentation
Documentation signals are not tracked yet for this package.
Downloads
Dependencies
Code & Tests
Datasets
People & History
- 0.2.0Latest2026-07-23 · current release · diff ↗ ·
removed 1 export
riem.plot.3dadded 6 exports
riem.lm, riem.original.scale, riem.pca, riem.plot3d, riem.similarities.dbscan, riem.similarities.isomap - 0.1.12026-07-17
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-17
- Total releases
- 2 / 1 yrs
- License
- BSD_3_clause + file LICENSE OSI
- Download size
- 1.9 MB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("riemannianStats")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.