Simultaneous Non-Gaussian Component Analysis
Match the colums of Mx and My
Average Mj for Mx and My Here subjects are by rows, columns correspond...
Calculates the sum of the JB scores across all components, useful for ...
Returns square root of the precision matrix for whitening
create graph dataset with netmat and mmp_order a data.frame called wit...
Curvilinear algorithm based on C code with r0 joint components
Curvilinear algorithm with r0 joint components
Estimate mixing matrix from estimates of components
Generate initialization from specific space
Calculate the power of a square matrix
Greedy Match
Decompose the original data through LNGCA method.
match ICA
find the number of non-Gaussian components in the data.
Orthogonalization of matrix
Permutation test to get joint components ranks
Permutation test with Greedymatch
Permutation invariant mean squared error
Sign change for S matrix to image
SImultaneous Non-Gaussian Component analysis for data integration.
Standardization with double centered and column scaling
Convert angle vector into orthodox matrix
tiltedgaussian
Create network matrices from vectorized lower diagonals vec2net tran...
Whitening Function
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>.