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‎.Rbuildignore‎

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^.*\.Rproj$
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^\.Rproj\.user$
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^\.github$
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^LICENSE\.md$

‎.github/workflows/R-CMD-check.yaml‎

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# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
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# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
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on:
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push:
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branches: [main, master]
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pull_request:
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branches: [main, master]
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name: R-CMD-check.yaml
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permissions: read-all
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jobs:
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R-CMD-check:
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runs-on: ${{ matrix.config.os }}
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name: ${{ matrix.config.os }} (${{ matrix.config.r }})
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strategy:
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fail-fast: false
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matrix:
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config:
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- {os: macos-latest, r: 'release'}
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- {os: windows-latest, r: 'release'}
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- {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'}
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- {os: ubuntu-latest, r: 'release'}
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- {os: ubuntu-latest, r: 'oldrel-1'}
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env:
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GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
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R_KEEP_PKG_SOURCE: yes
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steps:
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- uses: actions/checkout@v4
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- uses: r-lib/actions/setup-pandoc@v2
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- uses: r-lib/actions/setup-r@v2
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with:
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r-version: ${{ matrix.config.r }}
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http-user-agent: ${{ matrix.config.http-user-agent }}
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use-public-rspm: true
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- uses: r-lib/actions/setup-r-dependencies@v2
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with:
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extra-packages: any::rcmdcheck
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needs: check
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- uses: r-lib/actions/check-r-package@v2
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with:
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upload-snapshots: true
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build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

‎.gitignore‎

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*.Rproj
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.DS_Store
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.RData
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.Rhistory
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.Rproj.user
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.Ruserdata
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.httr-oauth

‎DESCRIPTION‎

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Package: grasps
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Type: Package
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Title: Groupwise Regularized Adaptive Sparse Precision Solution
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Version: 0.1.0
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Authors@R: c(
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person(given = "Shiying", family = "Xiao",
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email = "shiying.xiao@outlook.com",
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role = c("aut", "cre"),
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comment = c(ORCID = "0000-0002-8846-3258")),
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person(given = "Jun", family = "Yan",
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email = "jun.yan@uconn.edu",
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role = "aut",
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comment = c(ORCID = "0000-0003-4401-7296")),
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person(given = "Panpan", family = "Zhang",
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email = "panpan.zhang@vumc.org",
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role = "aut",
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comment = c(ORCID = "0000-0002-8211-5930")))
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Maintainer: Shiying Xiao <shiying.xiao@outlook.com>
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Description: Provides a unified framework for sparse-group regularization and
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precision matrix estimation in Gaussian graphical models. It implements
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multiple sparse-group penalties, including sparse-group lasso, sparse-group
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adaptive lasso, sparse-group SCAD, and sparse-group MCP, and solves them
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efficiently using ADMM-based optimization. The package is designed for
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high-dimensional network inference where both sparsity and group structure
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are present.
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License: GPL (>= 3)
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URL: https://github.com/Carol-seven/grasps
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BugReports: https://github.com/Carol-seven/grasps/issues
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Encoding: UTF-8
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LazyData: true
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Imports: Rcpp, Rdpack
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LinkingTo: Rcpp, RcppArmadillo
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RoxygenNote: 7.3.3
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RdMacros: Rdpack

‎LICENSE.md‎

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‎NAMESPACE‎

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# Generated by roxygen2: do not edit by hand
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export(sggm)
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importFrom(Rcpp,evalCpp)
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importFrom(Rdpack,reprompt)
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importFrom(stats,cov)
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useDynLib(grasps, .registration = TRUE)

‎R/RcppExports.R‎

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# Generated by using Rcpp::compileAttributes() -> do not edit by hand
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# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
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ADMMsggm <- function(S, group_idx, penalty, diag_ind, diag_grp, diag_include, lambda, alpha, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter) {
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.Call(`_grasps_ADMMsggm`, S, group_idx, penalty, diag_ind, diag_grp, diag_include, lambda, alpha, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter)
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}
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ADMMsggm_CV <- function(X, group_idx, penalty, diag_ind, diag_grp, diag_include, lambdas, alphas, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter, kfold) {
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.Call(`_grasps_ADMMsggm_CV`, X, group_idx, penalty, diag_ind, diag_grp, diag_include, lambdas, alphas, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter, kfold)
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}
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ADMMsggm_IC <- function(S, group_idx, penalty, diag_ind, diag_grp, diag_include, lambdas, alphas, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter, crit, n, ebic_tuning) {
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.Call(`_grasps_ADMMsggm_IC`, S, group_idx, penalty, diag_ind, diag_grp, diag_include, lambdas, alphas, gamma, rho, tau_incr, tau_decr, nu, tol_abs, tol_rel, maxiter, crit, n, ebic_tuning)
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}
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soft_matrix <- function(M, Thres) {
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.Call(`_grasps_soft_matrix`, M, Thres)
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}
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‎R/grasps-package.R‎

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#' grasps
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#'
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#' Groupwise Regularized Adaptive Sparse Precision Solution
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#'
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#' @docType _PACKAGE
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#' @importFrom Rcpp evalCpp
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#' @useDynLib grasps, .registration = TRUE
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#' @name grasps
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#' @noRd
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NULL

‎R/line_search_lambda_max.R‎

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#' Line search for the sharp upper bound lambda_max
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#'
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#' @description
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#' Given a list of off-diagonal blocks \code{blockList} from the sample
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#' covariance (or a related matrix), this routine finds a **sharp**
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#' \eqn{\lambda_{\max}} by bisection so that the KKT condition
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#' \deqn{\Vert\text{soft}(B, \lambda\alpha)\Vert_F \leq \lambda(1-\alpha)}
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#' holds for every block \eqn{B} in \code{blockList}, where
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#' \eqn{\text{soft}(\cdot, t)} denotes elementwise soft-thresholding with
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#' threshold \eqn{t}.
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#'
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#' @param blockList A list of numeric matrices where each element is a block
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#' used in the group KKT check.
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#'
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#' @param lambda.safe A numeric scalar representing a safe (but possibly loose)
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#' upper bound for \eqn{\lambda_{\max}}. If the value is negative, the search
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#' starts from 1.
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#'
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#' @param alpha A numeric scalar in (0, 1) specifying the mixture weight between
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#' the individual (L1) and group (L2) penalties in sparse-group formulations.
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#'
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#' @param growiter An integer specifying the maximum number of exponential
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#' growth steps during the initial search for an admissible upper bound
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#' \eqn{\lambda_{\max}}.
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#'
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#' @param tol A positive numeric scalar specifying the relative tolerance for
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#' the bisection stopping rule on the interval width.
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#'
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#' @param maxiter An integer specifying the maximum number of bisection
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#' iterations.
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#'
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#' @details
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#' The procedure first grows an upper bound starting from \code{lambda.safe}
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#' (or 1 if that is non-positive), doubling until the KKT check passes or a
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#' growth cap is reached, and then performs bisection between the current
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#' lower/upper bounds until the interval width is below
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#' \code{tol * max(1, lambda_upper)} or \code{maxiter} is hit.
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#'
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#' @return
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#' A numeric scalar representing the bisection-refined upper bound
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#' \eqn{\lambda_{\max}}.
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#'
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#' @noRd
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line_search_lambda_max <- function(blockList, lambda.safe, alpha, growiter, tol, maxiter) {
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stopifnot(is.list(blockList), length(blockList) > 0, alpha > 0, alpha < 1)
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KKTcheck <- function(lambda) {
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for (B in blockList) {
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Thres <- matrix(lambda*alpha, nrow(B), ncol(B))
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if (norm(soft_matrix(B, Thres), "F") > lambda*(1-alpha)+tol) {
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return(FALSE)
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}
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}
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return(TRUE)
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}
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lambda_upper <- if (lambda.safe > 0) lambda.safe else 1
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grow <- 0
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while (!KKTcheck(lambda_upper) && grow < growiter) {
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lambda_upper <- 2 * lambda_upper
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grow <- grow + 1
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}
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lambda_lower <- 0
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iter <- 0
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while (iter < maxiter && (lambda_upper-lambda_lower) > tol*max(1,lambda_upper)) {
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iter <- iter + 1
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lambda_mid <- (lambda_lower + lambda_upper) / 2
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if (KKTcheck(lambda_mid)) {
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lambda_upper <- lambda_mid
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} else {
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lambda_lower <- lambda_mid
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}
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}
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return(lambda_upper)
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}
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