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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).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.