Principal Component Analysis in High-Dimensional Data
Overview
In high-dimensional settings: Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model. Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors. Adjust the shrinkage bias in the predicted PC scores. Dey, R. and Lee, S. (2019) doi:10.1016/j.jmva.2019.02.007.
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Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Rounak Dey <deyrnk@umich.edu>’
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Authors@R: c(person(given = "Rounak",
family = "Dey",
role = c("aut", "cre"),
email = "deyrnk@umich.edu"),
person(given = "Seunggeun",
family = "Lee",
role = "aut"))
as necessary.- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 4
- Examples that run
- 100%3 run in a plain check
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- Return-value docs
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- Help pages citing references
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- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.1.5Latest
- RR 4.0.0 released · 2020-04-24
- 1.1.32019-10-23 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.0.02016-08-02
- RR 3.3.0 released · 2016-05-03
Package metadata
- First published
- 2016-08-02
- Total releases
- 3 / 10 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.0.0
- Bundled data
- 7.7 KB / 1 file
- Download size
- 17 KB
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