Package: singR 0.1.3

Liangkang Wang

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]

singR_0.1.3.tar.gz
singR_0.1.3.tar.gz(r-4.7-arm64)singR_0.1.3.tar.gz(r-4.7-x86_64)singR_0.1.3.tar.gz(r-4.6-arm64)singR_0.1.3.tar.gz(r-4.6-x86_64)
singR_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
singR/json (API)

# Install 'singR' in R:
install.packages('singR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

2.60 score 40 scripts 227 downloads 18 exports 63 dependencies

Last updated from:923257c201. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK185
linux-devel-x86_64OK222
source / vignettesOK254
linux-release-arm64OK175
linux-release-x86_64OK185
wasm-releaseOK160

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

Dependencies:cliclueclustercodetoolscpp11crayonDBIdplyrfarverforcatsforeachgamgenericsGGallyggplot2ggstatsgluegtablehmsICSICSNPICtestisobanditeratorsJADElabelinglatticelifecyclemagrittrMASSMatrixminqamitoolsmvtnormnumDerivpatchworkpillarpkgconfigpngprettyunitsprogresspurrrR6RColorBrewerRcppRcppArmadilloRcppRollrlangS7scalesstringistringrsurveysurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrxtszoo

singR: An R package for Simultaneous non-Gaussian Component Analysis for data integration

Rendered fromsingR-tutorial.Rmdusingknitr::rmarkdownon Jun 12 2026.

Last update: 2025-01-27
Started: 2022-08-22

Readme and manuals

Help Manual

Help pageTopics
Calculate the power of a square matrix%^%
Match the colums of Mx and MyangleMatchICA
Average Mj for Mx and My Here subjects are by rows, columns correspond to componentsaveM
Calculates the sum of the JB scores across all components, useful for determining rho.calculateJB
Returns square root of the precision matrix for whiteningcovwhitener
create graph dataset with netmat and mmp_order a data.frame called with vectorization of reordered netmat by mmp_order.create.graph.long
Curvilinear algorithm with r0 joint componentscurvilinear
Curvilinear algorithm based on C code with r0 joint componentscurvilinear_c
Estimate mixing matrix from estimates of componentsest.M.ols
Data for simulation example 1exampledata
Generate initialization from specific spacegen.inits
Greedy Matchgreedymatch
Decompose the original data through LNGCA method.lngca
match ICAmatchICA
find the number of non-Gaussian components in the data.NG_number
Orthogonalization of matrixorthogonalize
Permutation test to get joint components rankspermmatRank_joint
Permutation test with GreedymatchpermTestJointRank
Permutation invariant mean squared errorpmse
Sign change for S matrix to imagesignchange
SImultaneous Non-Gaussian Component analysis for data integration.singR
Standardization with double centered and column scalingstandard
Convert angle vector into orthodox matrixtheta2W
tiltedgaussiantiltedgaussian
Create network matrices from vectorized lower diagonals 'vec2net' transfer the matrix vectorized lower diagonals into net to show the component image.vec2net
Whitening Functionwhitener