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

• 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

• 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).