| PAFit-package | Generative Mechanism Estimation in Temporal Complex Networks |
| as.PAFit_net | Converting an edgelist matrix to a PAFit_net object |
| coauthor.author_id | A collaboration network between authors of papers in the field of complex networks with article time-stamps |
| coauthor.net | A collaboration network between authors of papers in the field of complex networks with article time-stamps |
| coauthor.truetime | A collaboration network between authors of papers in the field of complex networks with article time-stamps |
| ComplexNetCoauthor | A collaboration network between authors of papers in the field of complex networks with article time-stamps |
| from_igraph | Convert an igraph object to a PAFit_net object |
| from_networkDynamic | Convert a networkDynamic object to a PAFit_net object |
| generate_BA | Simulating networks from the generalized Barabasi-Albert model |
| generate_BB | Simulating networks from the Bianconi-Barabasi model |
| generate_ER | Simulating networks from the Erdos-Renyi model |
| generate_fit_only | Simulating networks from the Caldarelli model |
| generate_net | Simulating networks from preferential attachment and fitness mechanisms |
| generate_simulated_data_from_estimated_model | Generating simulated data from a fitted model |
| get_statistics | Getting summarized statistics from input data |
| graph_from_file | Read file to a PAFit_net object |
| graph_to_file | Write the graph in a PAFit_net object to file |
| Jeong | Jeong's method for estimating the preferential attachment function |
| joint_estimate | Joint inference of attachment function and node fitnesses |
| Newman | Corrected Newman's method for estimating the preferential attachment function |
| only_A_estimate | Estimating the attachment function in isolation by PAFit method |
| only_F_estimate | Estimating node fitnesses in isolation |
| PAFit | Generative Mechanism Estimation in Temporal Complex Networks |
| PAFit_data | Getting summarized statistics from input data |
| PAFit_oneshot | Estimating the nonparametric preferential attachment function from one single snapshot. |
| plot.Full_PAFit_result | Plotting the estimated attachment function and node fitness |
| plot.PAFit_net | Plot a 'PAFit_net' object |
| plot.PAFit_result | Plotting the estimated attachment function and node fitness of a 'PAFit_result' object |
| plot.PA_result | Plotting the estimated attachment function |
| plot_contribution | Plotting contributions calculated from the observed data and contributions calculated from simulated data |
| print.CV_Data | Printing simple information of the cross-validation data |
| print.CV_Result | Printing simple information of the cross-validation result |
| print.Full_PAFit_result | printing information on the estimation result |
| print.PAFit_data | Printing simple information on the statistics of the network stored in a 'PAFit_data' object |
| print.PAFit_net | Printing simple information of a 'PAFit_net' object |
| print.PAFit_result | printing information on the estimation result stored in a 'PAFit_result' object |
| print.PA_result | Printing information of the estimated attachment function |
| summary.CV_Data | Printing summary information of the cross-validation data |
| summary.CV_Result | Output summary information of the cross-validation result |
| summary.Full_PAFit_result | Summary information on the estimation result |
| summary.PAFit_data | Output summary information on the statistics of the network stored in a 'PAFit_data' object |
| summary.PAFit_net | Summary information of a 'PAFit_net' object |
| summary.PAFit_result | Output summary information on the estimation result stored in a 'PAFit_result' object |
| summary.PA_result | Summary of the estimated attachment function |
| test_linear_PA | Fitting various distributions to a degree vector |
| to_igraph | Convert a PAFit_net object to an igraph object |
| to_networkDynamic | Convert a PAFit_net object to a networkDynamic object |