| inferCSN-package | inferCSN: Inferring Cell-Specific Gene Regulatory Network |
| acc.calculate | ACC calculate |
| auc.calculate | AUC value calculate |
| calculate.gene.rank | Calculate and rank TFs in network |
| check.parameters | Check input parameters |
| coef.SRM_fit | Extracts a specific solution in the regularization path |
| coef.SRM_fit_CV | Extracts a specific solution in the regularization path |
| contrast.networks | contrast.networks |
| crossweight | Perform crossweighting |
| crossweight_params | estimates min and max values for crossweighting for now assumes uniform cell density across pseudotime/only considers early time this needs to be refined if it's to be useful... |
| dynamic.networks | Plot of dynamic networks |
| example_ground_truth | Example ground truth data |
| example_matrix | Example matrix data |
| example_meta_data | Example meta data |
| filter_sort_matrix | Filter and sort matrix |
| inferCSN | Inferring Cell-Specific Gene Regulatory Network |
| inferCSN-method | Inferring Cell-Specific Gene Regulatory Network |
| model.fit | Fit a sparse regression model |
| net.format | Format weight table |
| network.heatmap | The heatmap of network |
| normalization | normalization |
| predict.SRM_fit | Predict Response |
| predict.SRM_fit_CV | Predict Response |
| prepare.performance.data | prepare.performance.data |
| print.SRM_fit | Prints a summary of model.fit |
| print.SRM_fit_CV | Prints a summary of model.fit |
| single.network | Construct network for single gene |
| sparse.regression | Sparse regression model |
| table.to.matrix | Switch weight table to matrix |
| weight_filter | weight_filter |