SpatPCA: Regularized Principal Component Analysis for Spatial Data

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).

Package details

AuthorWen-Ting Wang, Hsin-Cheng Huang
Date of publication2017-03-18 00:20:28 UTC
MaintainerWen-Ting Wang <>
Package repositoryView on CRAN
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SpatPCA documentation built on May 29, 2017, 9:03 a.m.