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:Cansu Alakus [aut, cre], Denis Larocque [aut], Aurelie Labbe [aut], Hemant Ishwaran [ctb], Udaya B. Kogalur [ctb], Intel Corporation [cph], Keita Teranishi [ctb]

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'))

Peer review:

Bug tracker:https://github.com/calakus/rfcca/issues

Uses libs:
  • openblas– Optimized BLAS
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • data - Generated example data

On CRAN:

3.70 score 1 stars 3 scripts 140 downloads 8 exports 54 dependencies

Last updated 9 months agofrom:70f6dc2d76. Checks:OK: 8 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-win-x86_64NOTENov 02 2024
R-4.5-linux-x86_64OKNov 02 2024
R-4.4-win-x86_64OKNov 02 2024
R-4.4-mac-x86_64OKNov 02 2024
R-4.4-mac-aarch64OKNov 02 2024
R-4.3-win-x86_64OKNov 02 2024
R-4.3-mac-x86_64OKNov 02 2024
R-4.3-mac-aarch64OKNov 02 2024

Exports:global.significanceplot.vimpplot.vimp.rfccapredict.rfccaprint.rfccarfccavimpvimp.rfcca

Dependencies:ashbitopsCCAcliclustercolorspacedeSolvedotCall64fansifarverfdafdsfieldsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitmagrittrmapsMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmepcaPPpillarpkgconfigPMApracmaR6rainbowRColorBrewerRcppRCurlrlangscalesspamtibbleutf8vctrsviridisLitewithr

RFCCA: Random Forest with Canonical Correlation Analysis

Rendered fromRFCCA.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2024-02-06
Started: 2020-09-30