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.
- Version1.3.5
- R version≥ 3.4.0
- LicenseGPL-3
- Needs compilation?Yes
- Last release11/13/2023
Documentation
Team
Wen-Ting Wang
Hsin-Cheng Huang
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Insights
Last 30 days
This package has been downloaded 214 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,037 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 24, 2024 with 41 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports3 packages
- Suggests15 packages
- Linking To3 packages