Man pages for ntwk
Statistical Inference for Network Time Series
| augmented_diag | Returns an offset diagonal matrix. |
| bm_compound_poisson | Generates a (correlated) Brownian motion path with correlated... |
| bm_compound_poisson_ghyp | Generates a (correlated) Brownian motion path with correlated... |
| check_thetas | Checks that '|theta_1| <= theta_2'. |
| clean_data | Clean data using STL / Loess function. |
| concatenate_col | Concatenate 'col_vec' columnwise 'n' times |
| construct_path | Generates a single Graph Ornstein-Uhlenbeck path with the... |
| core_node_mle | Computes the MLE numerator and denominator of a batch of data |
| correlated_brownian_noise | Generates multidimensional correlated Brownian motion... |
| data_filtering | Filters the data above the thresholds (set to zero).... |
| fasen_regression | Fasen's multivariate OU least squares regression doi:... |
| fit_bm_compound_poisson | Fit Brownian motion mixture with Gaussian jumps. |
| fit_ghyp_diffusion | Fit a Generalised Hyperbolic distribution. |
| fully_connected_network | Fully-connected graph. |
| grad_likelihood_fn | Gradient of the GrOU likelihood function with penalty. |
| grou_mle | Computes the GrOUs MLE of a batch of data |
| grou_regularisation | Regularisation schemes for the GrOU process that implements a... |
| isolated_network | Creates adjacency matrix with max degree zero. |
| lattice_network | Lattice/grid-like network with max degree of four across all... |
| levy_recovery | Apply the Levy recovery on data with (fitted)... |
| likelihood_fn | Likelihood function for the GrOU process with penalty. |
| make_node_mle | Constructs the MLE from its components |
| node_mle_components | Returns the components (i.e. numerator and denominator) of... |
| node_mle_long | Constructs the Node MLE with jump thresholding |
| polymer_network | Creates adjacency matrix with min degree one and max degree... |
| row_normalised | Normalise a square matrix by dividing elements by the sum of... |
