Nothing
SpatPCA: Regularized Principal Component Analysis for Spatial Data
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.
Package details |
|
|---|---|
| Author | Wen-Ting Wang [aut, cre] (<https://orcid.org/0000-0003-3051-7302>), Hsin-Cheng Huang [aut] (<https://orcid.org/0000-0002-5613-349X>) |
| Maintainer | Wen-Ting Wang <egpivo@gmail.com> |
| License | GPL-3 |
| Version | 1.3.5 |
| URL | https://github.com/egpivo/SpatPCA |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the SpatPCA package in your browser
Any scripts or data that you put into this service are public.
