README.md
In rounakdey/hdpca: Principal Component Analysis in High-Dimensional Data
hdpca - Principal Component Analysis in High-Dimensional Data
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.
rounakdey/hdpca documentation built on Jan. 21, 2021, 11:06 a.m.
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hdpca - Principal Component Analysis in High-Dimensional Data
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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