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:Liangkang Wang [aut, cre], Irina Gaynanova [aut], Benjamin Risk [aut]

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18 exports 0.09 score 80 dependencies 29 scripts 3.6k downloads

Last updated 7 months agofrom:9f8f6034b1. Checks:OK: 9. Indexed: yes.

Exports:%^%aveMcalculateJBcreate.graph.longcurvilinearcurvilinear_cest.M.olsgen.initsgreedymatchlngcaNG_numberpermTestJointRankpmsesignchangesingRstandardvec2netwhitener

Dependencies:backportsbitbit64broombroom.helperscardsclicliprclueclustercodetoolscolorspacecpp11crayonDBIdplyrfansifarverforcatsforeachgamgenericsGGallyggplot2ggstatsgluegtablehavenhmsICSICSNPICtestisobanditeratorsJADElabelinglabelledlatticelifecyclemagrittrMASSMatrixmgcvminqamitoolsmunsellmvtnormnlmenumDerivpatchworkpillarpkgconfigplyrpngprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadilloRcppRollreadrrlangscalesstringistringrsurveysurvivaltibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrxtszoo