R/PCLassoReg-package.R

In PCLassoReg: Group Regression Models for Risk Protein Complex Identification

#' @title \packageTitle{PCLassoReg}
#' @description \packageDescription{PCLassoReg}
#' @details
#' The PCLasso model accepts a protein expression
#' matrix, survival data, and protein complexes for training the prognostic
#' model, and makes predictions for new samples and identifies risk protein
#' complexes associated with survival.
#'
#' The PCLasso2 model accepts a protein expression matrix, a response vector,
#' and protein complexes for training the classification model, and makes
#' predictions for new samples and identifies risk protein complexes associated
#' with classes.
#'
#' Both PCLasso and PCLasso2 use \code{grLasso} as the penalty function. The
#' other two penalties \code{grSCAD} and \code{grMCP} can also be used for model
#' construction and risk protein complex identification. The package also
#' provides methods for plotting coefficient paths and cross-validation curves.
#'
#' @references
#' PCLasso2: a protein complex-based, group Lasso-logistic model for risk
#' protein complex discovery. To be published.
#'
#' PCLasso: a protein complex-based group lasso-Cox model for accurate prognosis
#' and risk protein complex discovery. Brief Bioinform, 2021.
#'
#' Park, H., Niida, A., Miyano, S. and Imoto, S. (2015) Sparse overlapping group
#' lasso for integrative multi-omics analysis. Journal of computational biology:
#' a journal of computational molecular cell biology, 22, 73-84.
#'
#' @docType package
#' @name PCLassoReg
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