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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3.8 |
rolling linux/jammy R-4.5 | SpatPCA_1.3.8.tar.gz |
426.2 KiB |
1.3.8 |
rolling linux/noble R-4.5 | SpatPCA_1.3.8.tar.gz |
431.5 KiB |
1.3.8 |
rolling source/ R- | SpatPCA_1.3.8.tar.gz |
278.2 KiB |
1.3.8 |
latest linux/jammy R-4.5 | SpatPCA_1.3.8.tar.gz |
426.2 KiB |
1.3.8 |
latest linux/noble R-4.5 | SpatPCA_1.3.8.tar.gz |
431.5 KiB |
1.3.8 |
latest source/ R- | SpatPCA_1.3.8.tar.gz |
278.2 KiB |
1.3.8 |
2026-04-26 source/ R- | SpatPCA_1.3.8.tar.gz |
278.2 KiB |
1.3.8 |
2026-04-23 source/ R- | SpatPCA_1.3.8.tar.gz |
278.2 KiB |
1.3.8 |
2026-04-09 windows/windows R-4.5 | SpatPCA_1.3.8.zip |
835.7 KiB |
1.3.5 |
2025-04-20 source/ R- | SpatPCA_1.3.5.tar.gz |
1.2 MiB |