nsdr
0.1.1Nonlinear Sufficient Dimension Reduction
Overview
Provides tools to implement both unsupervised and supervised nonlinear dimension reduction methods. Principal Component Analysis (PCA), Sliced Inverse Regression (SIR), and Sliced Average Variance Estimation (SAVE) are useful methods to reduce the dimensionality of covariates. However, they produce linear combinations of covariates. Kernel PCA, generalized SIR, and generalized SAVE address this problem by extending the applicability of the dimension reduction problem to nonlinear settings. This package includes a comprehensive algorithm for kernel PCA, generalized SIR, and generalized SAVE, including methods for choosing tuning parameters and some essential functions.
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Code & Tests
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People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-11-28the request of the maintainer
- RR 4.2.0 released · 2022-04-22
- 0.1.12021-06-03 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 0.1.02021-05-11
- RR 4.0.0 released · 2020-04-24
Package metadata
- Total releases
- 2
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 217 KB / 3 files
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