RFCCA

Random Forest with Canonical Correlation Analysis (RFCCA) is a random forest method for estimating the canonical correlations between two sets of variables depending on the subject-related covariates. The trees are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. The method is described in Alakus et al. (2021) doi:10.1093/bioinformatics/btab158. 'RFCCA' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the version 2.9.3. The custom splitting rule feature is utilised to apply the proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' by default, contingent upon the support provided by the target architecture and operating system. In this package, 'LAPACK' and 'BLAS' libraries are used for matrix decompositions.

Last 30 days

This package has been downloaded 11 times in the last 30 days. Not quite a landslide, but certainly not negligible. Keep it up! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 times.

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Last 365 days

This package has been downloaded 1,638 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Sep 11, 2024 with 24 downloads.

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