R/RSCA.R

In ZhengguoGu/RegularizedSCA: Regularized Simultaneous Component Based Data Integration

#' RSCA: A package for regularized simultaneous component analysis (SCA) for data integration.
#'
#' The RSCA provides the following functions for performing regularized SCA.
#' 
#' @section DISCOsca:
#' A DISCO-SCA procedure for identifying common and distinctive components.
#' @section TuckerCoef:
#' Tucker's coefficient of congruence between columns but after accounting for permutational freedom and reflections.
#' @section VAF:
#' Proportion of variance accounted for (VAF) for each block and each principal component.
#' @section cv_sparseSCA:
#' A K-fold cross-validation procedure when common/distinctive processes are unknown with Lasso and Group Lasso penalties.
#' @section cv_structuredSCA:
#' A K-fold cross-validation procedure when common/distinctive processes are known, with a Lasso penalty.
#' @section maxLGlasso:
#' An algorithm for determining the smallest values for Lasso and Group Lasso tuning parameters that yield all zeros.
#' @section mySTD:
#' Standardize the given data matrix per column, over the rows.
#' @section pca_gca:
#' PCA-GCA method for selecting the number of common and distinctive components.
#' @section sparseSCA:
#' Variable selection with Lasso and Group Lasso with a multi-start procedure. 
#' @section structuredSCA:
#' Variable selection algorithm with a predefined component loading structure.
#' @section undoShrinkage:
#' Undo shrinkage (on estimated component loading matrix).
#' @docType package
#' @name RSCA
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