Man pages for mlondschien/hdcd
High - Dimensional Change Point Detection
| best_split_function_from_gain_function | Get a best_split_function from a gain_function |
| binary_segmentation | Binary Segmentation |
| ChainNetwork | ChainNetwork |
| classifier_best_split_function | Get a get_best_split function from a classifier |
| classifier_gain_function | Closure generating function to calculate gains for splits... |
| classifier_loglikelihood | Loglikelihood from classifier |
| compare_change_points | Rand type performance indices |
| create_model | Create GGM with changepoints |
| delete_values | Delete values from a design matrix |
| DiagMatrix | DiagMatrix |
| draw_segments | Draw segment (-boundaries) for WBS or BS |
| get_change_points_from_tree | Get Change Points from a binary_segmentation_tree |
| get_cov_mat | Calculate a covariance matrix |
| get_glasso_fit | Get a glasso fit |
| get_splits | obtain all possible segmentation scenarios that can be... |
| glasso_cross_validation_function | Glasso cross validation function |
| glasso_gain_function | Closure generating function to calculate gains when splitting... |
| hdcd | High-dimensional change point detection |
| hdcd_control | Create an object of class hdcd_control to supply parameters... |
| kNN_best_split_function | Get a get_best_split function from the kNN classifier |
| kNNcd | k-Nearest Neighbor based change point detection |
| kNN_gain_function | kNN gain function |
| line_search | Line search optimisation algorithm |
| log_eps | Bounded approximation to the logarithm |
| loglikelihood | Negative loglikelihood of a multivariate normal |
| log_space | Logarithmically Scaled Sequence Generation |
| MoveEdges | MoveEdges |
| plot.binary_segmentation_tree | S3 Method for plotting an object of class... |
| plot_missingness_structure | Plot the missingness structure of a design matrix |
| print.binary_segmentation_tree | print.binary_segmentation_tree |
| RandomNetwork | RandomNetwork |
| RegrowNetwork | RegrowNetwork |
| RFcd | Random Forest change point detection |
| sample_folds | Sample Folds |
| ScaleNetwork | ScaleNetwork |
| section_search | Section search optimisation algorithm |
| shift_in_mean_and_variance | find best split shift in mean and variance |
| simulate_from_model | Simulate Observations from a model created by create_model |
| simulate_non_parametric | simulate (non-parametric) data sets |
| smooth_section_search | smooth section search optimisation algorithm |
| train_test_split | Divide data into training + testing data |
| two_step_search | Two step search optimisation algorithm |
