Package: singR 0.1.2
singR: Simultaneous Non-Gaussian Component Analysis
Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.
Authors:
singR_0.1.2.tar.gz
singR_0.1.2.tar.gz(r-4.5-noble)singR_0.1.2.tar.gz(r-4.4-noble)
singR_0.1.2.tgz(r-4.4-emscripten)singR_0.1.2.tgz(r-4.3-emscripten)
singR.pdf |singR.html✨
singR/json (API)
# Install 'singR' in R: |
install.packages('singR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- exampledata - Data for simulation example 1
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:9f8f6034b1. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 05 2024 |
R-4.5-linux-x86_64 | OK | Dec 05 2024 |
Exports:%^%aveMcalculateJBcreate.graph.longcurvilinearcurvilinear_cest.M.olsgen.initsgreedymatchlngcaNG_numberpermTestJointRankpmsesignchangesingRstandardvec2netwhitener
Dependencies:cliclueclustercodetoolscolorspacecpp11crayonDBIdplyrfansifarverforcatsforeachgamgenericsGGallyggplot2ggstatsgluegtablehmsICSICSNPICtestisobanditeratorsJADElabelinglatticelifecyclemagrittrMASSMatrixmgcvminqamitoolsmunsellmvtnormnlmenumDerivpatchworkpillarpkgconfigplyrpngprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadilloRcppRollrlangscalesstringistringrsurveysurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxtszoo