Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation
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Abstract (arXiv)
Discovering patterns of the complex high-dimensional data is a long-standing problem. Dimension Reduction (DR) and Intrinsic Dimension Estimation (IDE) are two fundamental thematic programs that facilitate geometric understanding of the data. We present Rdimtools - an R package that supports 133 DR and 17 IDE algorithms whose extent makes multifaceted scrutiny of the data in one place easier. Rdimtools is distributed under the MIT license and is accessible from CRAN, GitHub, and its package website, all of which deliver instruction for installation, self-contained examples, and API documentation.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 2.
5 papers.
- 2021Scikit-dimension: a Python package for intrinsic dimension estimationcited by 68
- 2025Scalable Geometric Learning with Correlation-Based Functional Brain Networkscited by 2
- 2021Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalencecited by 1
- 2024On consistent estimation of dimension valuescited by 0
- 2025A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesiscited by 0
- 2025A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis19 Sep 2025
- 2025Scalable Geometric Learning with Correlation-Based Functional Brain Networks31 Mar 2025
- 2024On consistent estimation of dimension values18 Dec 2024
- 2021Scikit-dimension: a Python package for intrinsic dimension estimation6 Sep 2021
- 2021Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence3 Jun 2021