cpmr 0.1.1

Enhancements

• Renamed internal constant networks and includes to corr_types and inc_edges, respectively, to better reflect their purpose. In addition, the documentation which mentioned networks has been updated to use correlation types to better reflect their meaning.
• Reorganized R/cpm.R so cpm() remains the entry-point function at the top, followed by print.cpm(), with internal helpers grouped below.
• Improved test architecture with explicit internal-helper coverage and complete-case fold invariants.

Maintenance

• Refactored the internal cpm() workflow into smaller helpers (normalize_inputs, resolve_include_cases, apply_confounds_regression, etc.) while preserving user-facing behavior.
• Removed the obsolete internal alias regress_counfounds().
• Removed broad Rfast namespace import in favor of explicit Rfast:: calls.
• Updated summary.cpm() edge summarization path to avoid mutating local object fields.

cpmr 0.1.0

New features

• Added summary() method to summarize the results of the CPM analysis (#8).
• Added tidy() method to tidy the results of the CPM analysis (#10).
• Support na_action argument in cpm() function to handle missing values in the input data (#2).

Enhancements

• Added params to cpm() output to store the input arguments (#14).
• Let "sum" be the default value for return_edges argument.
• Let the first two dimensions of edges in the output be edges and networks, respectively.
• Polish the print method of the cpm class.

cpmr 0.0.9

New features

• Added support for row/column matrix as input for behavior and confounds data.

Maintenance

• Added more data checks to ensure the input data are in the correct format.

cpmr 0.0.8

• Added return_edges argument to optionally set how to return edges in the output.

cpmr 0.0.7

• Convert back to older version of confounds treating.

cpmr 0.0.6

• Ensure confounds regression are now only used in feature selection.

cpmr 0.0.5

• Fixed confounds treatment. Now confounds are used in feature selection but not in model fitting.

cpmr 0.0.4

• Ensure sparsity threshold method work as expect.
• Some other improvements in code quality.

cpmr 0.0.3

• Keep observation names in the output.
• Check if observation names match between neural data and behavioral data.

cpmr 0.0.2

• Added support for confounding variables.

cpmr 0.0.1

• Initial commit to r-universe.