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Changelog

Source: NEWS.md

grasps 0.1.2

  • Fixed the tuning-parameter grid so that all combinations of user-supplied alpha and lambda values are evaluated.
  • Fixed automatic tuning-parameter generation so that a separate lambda sequence is constructed for each value of alpha using its corresponding lambda.max.
  • Validation of nlambda and lambda.min.ratio is now performed only when lambda = NULL.
  • Added a check for a non-positive or non-finite lambda.max to prevent the generation of invalid lambda sequences.
  • Fixed within-group weight generation in gen_prec_sbm() so that each edge weight is sampled once and assigned symmetrically to the corresponding entries of the precision matrix.
  • Added input validation for cond.target and support for empty graphs in gen_prec_sbm().
  • Clarified the roles of lambda.safe and lambda.max in the documentation of the automatic lambda-grid construction.
  • Expanded the documentation of the positive-definiteness and condition-number adjustment in gen_prec_sbm() and added relevant references.

grasps 0.1.1

CRAN release: 2026-05-02

  • Added function plot.adjmat().
  • Added function prec_to_adj().
  • Use d instead of p to denote the dimension.
  • Revised the estimator expression in the vignette.
  • Added NEWS file to record the changelog.

grasps 0.1.0

CRAN release: 2025-11-27

  • Initial release of the grasps package.
  • The grasps is a toolbox for precision matrix estimation with group structure.
  • Key features:
    • Unified regularization framework for sparse network learning.
    • Supports element-wise sparsity and group-wise shrinkage.
    • Includes both convex and non-convex penalties (e.g., adaptive lasso, SCAD, MCP).
    • Provides model selection tools (e.g., CV, EBIC, HBIC).
  • Designed for applications where variables exhibit grouped or modular relationships (e.g., brain networks, biological pathways).