NEWS
grasps 0.1.2 (2026-09-12)
- 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 (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 (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).