Package: RFCCA 2.0.0
RFCCA: Random Forest with Canonical Correlation Analysis
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.
Authors:
RFCCA_2.0.0.tar.gz
RFCCA_2.0.0.zip(r-4.5)RFCCA_2.0.0.zip(r-4.4)RFCCA_2.0.0.zip(r-4.3)
RFCCA_2.0.0.tgz(r-4.4-x86_64)RFCCA_2.0.0.tgz(r-4.4-arm64)RFCCA_2.0.0.tgz(r-4.3-x86_64)RFCCA_2.0.0.tgz(r-4.3-arm64)
RFCCA_2.0.0.tar.gz(r-4.5-noble)RFCCA_2.0.0.tar.gz(r-4.4-noble)
RFCCA_2.0.0.tgz(r-4.4-emscripten)RFCCA_2.0.0.tgz(r-4.3-emscripten)
RFCCA.pdf |RFCCA.html✨
RFCCA/json (API)
NEWS
| # Install 'RFCCA' in R: |
| install.packages('RFCCA', repos = c('https://calakus.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/calakus/rfcca/issues
- data - Generated example data
Last updated 12 months agofrom:13129b38b1. Checks:8 OK, 1 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 06 2025 |
| R-4.5-win-x86_64 | NOTE | Jan 06 2025 |
| R-4.5-linux-x86_64 | OK | Jan 06 2025 |
| R-4.4-win-x86_64 | OK | Jan 06 2025 |
| R-4.4-mac-x86_64 | OK | Jan 06 2025 |
| R-4.4-mac-aarch64 | OK | Jan 06 2025 |
| R-4.3-win-x86_64 | OK | Jan 06 2025 |
| R-4.3-mac-x86_64 | OK | Jan 06 2025 |
| R-4.3-mac-aarch64 | OK | Jan 06 2025 |
Exports:global.significanceplot.vimpplot.vimp.rfccapredict.rfccaprint.rfccarfccavimpvimp.rfcca
Dependencies:ashbitopsCCAcliclustercolorspacedeSolvedotCall64fansifarverfdafdsfieldsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrmapsMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigPMApracmaR6rainbowRColorBrewerRcppRCurlrlangscalesspamtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| RFCCA: A package for computing canonical correlations depending on subject-related covariates with random forests | RFCCA-package |
| Generated example data | data |
| Global significance test | global.significance |
| Plot variable importance measures for rfcca objects | plot.vimp plot.vimp.rfcca |
| Predict method for rfcca objects | predict.rfcca |
| Print summary output of a RFCCA analysis | print.rfcca |
| Random Forest with Canonical Correlation Analysis | rfcca |
| Variable importance for rfcca objects | vimp vimp.rfcca |
