SpatPCA (1.1.1.2)

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Regularized Principal Component Analysis for Spatial Data.

https://github.com/egpivo/SpatPCA
http://cran.r-project.org/web/packages/SpatPCA

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data (Wang and Huang, 2017).

Maintainer: Wen-Ting Wang
Author(s): Wen-Ting Wang, Hsin-Cheng Huang

License: GPL-2

Uses: Rcpp, RcppParallel

Released 4 months ago.


6 previous versions

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