R/Tlasso-package.r

In Tlasso: Non-Convex Optimization and Statistical Inference for Sparse Tensor Graphical Models

#' Non-Convex Optimization and Statistical Inference for Sparse Tensor Graphical Models
#' 
#' An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
#' 
#' 
#' @details 
#' \tabular{ll}{
#'   Package: \tab Tlasso \cr
#'   Type: \tab Package \cr
#'   Date \tab 2016-09-17 \cr
#'   License: \tab GPL (>= 2) \cr
#' }
#' 
#' @author 
#' \tabular{l}{
#'   Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng. \cr
#'   Maintainer: Xiang Lyu <xianglyu@berkeley.edu>
#' }
#' 
#' @references 
#'    \tabular{l}{
#'          Fan J, Feng Y, Wu Y. \emph{Network exploration via the adaptive LASSO and SCAD penalties.} The annals of applied statistics, 2009, 3(2): 521. \cr
#'          Friedman J, Hastie T, Tibshirani R. \emph{Sparse inverse covariance estimation with the graphical lasso.} Biostatistics, 2008: 9.3: 432-441. \cr
#'          Lee W, Liu Y. \emph{Joint estimation of multiple precision matrices with common structures.} Journal of Machine Learning Research, 2015, 16: 1035-1062. \cr
#'          Li H, Gui J. \emph{Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks.} Biostatistics, 2006, 7(2): 302-317. \cr
#'          Lyu X, Sun W, Wang Z, Liu H, Yang J, Cheng G. \emph{Tensor Graphical Model: Non-convex Optimization and Statistical Inference.} IEEE transactions on pattern analysis and machine intelligence, 2019, 42(8): 2024-2037.

#'     }
#' @name Tlasso
#' @docType package
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