SPCAvRP
Sparse Principal Component Analysis via Random Projections
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) doi:10.48550/arXiv.1712.05630. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
- Version0.4
- R version≥ 3.0.0 parallel,
- LicenseGPL-3
- Needs compilation?No
- Last release05/03/2019
Team
Milana Gataric
Tengyao Wang
Richard J. Samworth
Insights
Last 30 days
This package has been downloaded 141 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 3 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 1,819 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 Sep 11, 2024 with 26 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.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package