A B C D E F G H I L M N O P Q R S T V W
| action_to_onehot | Convert Action Column to One-Hot Encoding |
| actor_endpoints | Tidy per-actor endpoint summary of a wide-format sequence dataset |
| ai_long | Human-AI Vibe Coding Interaction Data (Long Format) |
| as.data.frame.net_cluster_diagnostics | Cluster Diagnostics |
| as.data.frame.net_hypergraph_cluster | Spectral clustering of hypergraph vertices |
| as.data.frame.net_hypergraph_transduction | Transductive label spreading on a hypergraph |
| as.data.frame.state_freq | Plot State Frequency Distributions |
| association_rules | Discover Association Rules from Sequential or Transaction Data |
| as_htna | Build a grouped node-level network (htna) from data and a clustering |
| as_htna.default | Build a grouped node-level network (htna) from data and a clustering |
| as_htna.mcml | Build a grouped node-level network (htna) from data and a clustering |
| as_htna.net_mmm | Build a grouped node-level network (htna) from data and a clustering |
| as_netdifference | Coerce an inferential comparison to a network difference |
| as_netdifference.default | Coerce an inferential comparison to a network difference |
| as_netdifference.netdifference | Coerce an inferential comparison to a network difference |
| as_netdifference.net_bayes | Coerce an inferential comparison to a network difference |
| as_netobject | Coerce a network object to a Nestimate netobject |
| as_netobject.cograph_network | Coerce a network object to a Nestimate netobject |
| as_netobject.default | Coerce a network object to a Nestimate netobject |
| as_netobject.netobject | Coerce a network object to a Nestimate netobject |
| as_netobject.psychnet | Coerce a network object to a Nestimate netobject |
| as_networks | Promote a psychometric MCML result to a network group |
| as_networks.default | Promote a psychometric MCML result to a network group |
| as_networks.mcml_pc | Promote a psychometric MCML result to a network group |
| as_tna | Promote the Layers of an mcml to Networks |
| as_tna.default | Promote the Layers of an mcml to Networks |
| as_tna.mcml | Promote the Layers of an mcml to Networks |
| bayes_compare | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| betti_numbers | Betti Numbers |
| bipartite_groups | Hypergraph from bipartite group / event data |
| bootstrap_network | Bootstrap a Network Estimate |
| boot_glasso | Bootstrap for Regularized Partial Correlation Networks |
| bottleneck_distance | Bottleneck Distance Between Persistence Diagrams |
| build_atna | Build an Attention-Weighted Transition Network (ATNA) |
| build_clusters | Cluster Sequences by Dissimilarity |
| build_cna | Build a Co-occurrence Network (CNA) |
| build_cor | Build a Correlation Network |
| build_ftna | Build a Frequency Transition Network (FTNA) |
| build_gimme | GIMME: Group Iterative Multiple Model Estimation |
| build_glasso | Build a Graphical Lasso Network (EBICglasso) |
| build_hon | Build a Higher-Order Network (HON) |
| build_honem | Build HONEM Embeddings for Higher-Order Networks |
| build_hypa | Detect Path Anomalies via HYPA |
| build_hypergraph | Higher-order hypergraph from a network's clique structure |
| build_ising | Build an Ising Network |
| build_mcml | Build MCML from Raw Transition Data |
| build_mcml_pc | Multi-Cluster Multi-Level Aggregation for Psychometric Networks |
| build_mlvar | Build a Multilevel Vector Autoregression (mlVAR) network |
| build_mmm | Fit a Mixed Markov Model |
| build_mogen | Build Multi-Order Generative Model (MOGen) |
| build_network | Build a Network |
| build_pcor | Build a Partial Correlation Network |
| build_simplicial | Build a Simplicial Complex |
| build_tna | Build a Transition Network (TNA) |
| casedrop_reliability | Edge-weight Case-dropping Stability |
| centrality_stability | Centrality Stability Coefficient (CS-coefficient) |
| certainty | Analytic certainty of network edges (Bayesian Dirichlet-Multinomial) |
| chain_structure | Qualitative structure of a discrete-time Markov chain |
| chatgpt_srl | ChatGPT Self-Regulated Learning Scale Scores |
| clique_expansion | Clique expansion of a hypergraph |
| cluster_choice | Cluster Choice - sweep k, dissimilarity and method |
| cluster_diagnostics | Cluster Diagnostics |
| cluster_mmm | Cluster sequences using Mixed Markov Models |
| cluster_network | Cluster data and build per-cluster networks in one step |
| cluster_summary | Cluster Summary Statistics |
| coefs | Tidy coefficients from a fitted mlvar model |
| coefs.default | Tidy coefficients from a fitted mlvar model |
| coefs.net_mlvar | Tidy coefficients from a fitted mlvar model |
| compare_mmm | Compare MMM fits across different k |
| compare_model | Compare two networks descriptively |
| compare_model.cograph_network | Compare two networks descriptively |
| compare_model.matrix | Compare two networks descriptively |
| compare_model.netobject | Compare two networks descriptively |
| compare_model.netobject_group | Compare two networks descriptively |
| compare_networks | Compare two or more networks |
| comparison_tables | Tables of a network comparison |
| composites | Cluster Scores From a Psychometric MCML Fit |
| composites.mcml_pc | Cluster Scores From a Psychometric MCML Fit |
| convert_sequence_format | Convert Sequence Data to Different Formats |
| cooccurrence | Build a Co-occurrence Network |
| distribution_plot | State Distribution Plot Over Time |
| edge_differences | Tables of a network comparison |
| effects_table | Effect Table of a Fitted Outcome Model |
| entropy_bayes | Bayesian Transition Entropy |
| entropy_network | Transition Entropy Network |
| entropy_trajectory | Sliding-Window Transition Entropy Trajectory |
| estimate_network | Estimate a Network (Deprecated) |
| euler_characteristic | Euler Characteristic |
| evaluate_links | Evaluate Link Predictions Against Known Edges |
| extract_edges | Extract Edge List with Weights |
| extract_initial_probs | Extract Initial Probabilities from Model |
| extract_pathways | Cut an Event Log into Pathways |
| extract_transition_matrix | Extract Transition Matrix from Model |
| frequencies | Build a Transition Frequency Matrix |
| get_estimator | Retrieve a Registered Estimator |
| global_differences | Tables of a network comparison |
| group_regulation_long | Group Regulation in Collaborative Learning (Long Format) |
| human_long | Human-AI Vibe Coding Interaction Data (Long Format) |
| hypergraph_centrality | Hypergraph eigenvector centralities |
| hypergraph_cluster | Spectral clustering of hypergraph vertices |
| hypergraph_laplacian | Normalized hypergraph Laplacian |
| hypergraph_measures | Structural measures for a hypergraph |
| hypergraph_transduction | Transductive label spreading on a hypergraph |
| item_loadings | Item Diagnostics From a Psychometric MCML Fit |
| item_loadings.mcml_pc | Item Diagnostics From a Psychometric MCML Fit |
| learning_activities | Online Learning Activity Indicators |
| list_estimators | List All Registered Estimators |
| loading_stability | Composite-Weight Stability Under Case Resampling |
| long-data | Human-AI Vibe Coding Interaction Data (Long Format) |
| long_to_wide | Convert Long Format to Wide Sequences |
| macro_network | Cluster-Level Network, With One Cluster Expanded |
| magnitude_difference | Magnitude difference between the frequency and probability views |
| markov_order_test | Test the Markov order of a sequential process |
| markov_stability | Markov Stability Analysis |
| mark_first_state | Mark leading-NA cells with an explicit state label |
| mark_terminal_state | Mark terminal-NA cells with an explicit state label |
| mogen_transitions | Extract Transition Table from a MOGen Model |
| mosaic_analysis | Two-variable mosaic analysis (chi-square test + flat mosaic) |
| mosaic_plot | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.default | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.htna | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.matrix | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.mcml | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.netobject | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.netobject_group | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| mosaic_plot.table | Mosaic Plot of a Network's Transition or Co-occurrence Counts |
| nct | Network Comparison Test |
| network_metrics | Tables of a network comparison |
| network_reliability | Split-Half Reliability for Network Estimates |
| net_aggregate_weights | Aggregate Edge Weights |
| net_centrality | Compute Centrality Measures for a Network |
| net_deprune | Undo Network Pruning |
| net_deprune.default | Undo Network Pruning |
| net_deprune.netobject | Undo Network Pruning |
| net_deprune.netobject_group | Undo Network Pruning |
| net_edge_betweenness | Edge Betweenness Network |
| net_edge_betweenness.default | Edge Betweenness Network |
| net_edge_betweenness.netobject | Edge Betweenness Network |
| net_edge_betweenness.netobject_group | Edge Betweenness Network |
| net_prune | Prune a Network's Edges |
| net_prune.default | Prune a Network's Edges |
| net_prune.netobject | Prune a Network's Edges |
| net_prune.netobject_group | Prune a Network's Edges |
| net_pruning_details | Report Network Pruning Details |
| net_pruning_details.default | Report Network Pruning Details |
| net_pruning_details.netobject | Report Network Pruning Details |
| net_pruning_details.netobject_group | Report Network Pruning Details |
| net_reprune | Re-apply Network Pruning |
| net_reprune.default | Re-apply Network Pruning |
| net_reprune.netobject | Re-apply Network Pruning |
| net_reprune.netobject_group | Re-apply Network Pruning |
| node_differences | Tables of a network comparison |
| outcome_model | Model Unit-Level Outcomes from Sequence or Network Predictors |
| passage_time | Mean First Passage Times |
| pathways | Extract Pathways from Higher-Order Network Objects |
| pathways.netobject | Extract Pathways from Higher-Order Network Objects |
| pathways.net_association_rules | Extract Pathways from Higher-Order Network Objects |
| pathways.net_hon | Extract Pathways from Higher-Order Network Objects |
| pathways.net_hypa | Extract Pathways from Higher-Order Network Objects |
| pathways.net_link_prediction | Extract Pathways from Higher-Order Network Objects |
| pathways.net_mogen | Extract Pathways from Higher-Order Network Objects |
| path_counts | Count Path Frequencies in Trajectory Data |
| path_dependence | Per-Context Path Dependence at Order k |
| permutation | Permutation Test for Network Comparison |
| permutation_diagnostics | Does Nesting Bias a Permutation Test? |
| persistence_landscape | Persistence Landscape |
| persistent_homology | Persistent Homology |
| plot.boot_glasso | Bootstrap for Regularized Partial Correlation Networks |
| plot.chain_structure | Qualitative structure of a discrete-time Markov chain |
| plot.cluster_choice | Cluster Choice - sweep k, dissimilarity and method |
| plot.magnitude_difference | Magnitude difference between the frequency and probability views |
| plot.mcml_pc | Multi-Cluster Multi-Level Aggregation for Psychometric Networks |
| plot.mmm_compare | Compare MMM fits across different k |
| plot.mosaic_analysis | Two-variable mosaic analysis (chi-square test + flat mosaic) |
| plot.net_association_rules | Discover Association Rules from Sequential or Transaction Data |
| plot.net_bayes | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| plot.net_casedrop_reliability | Edge-weight Case-dropping Stability |
| plot.net_casedrop_reliability_group | Edge-weight Case-dropping Stability |
| plot.net_centrality | Compute Centrality Measures for a Network |
| plot.net_centrality_group | Compute Centrality Measures for a Network |
| plot.net_clustering | Cluster Sequences by Dissimilarity |
| plot.net_cluster_diagnostics | Cluster Diagnostics |
| plot.net_comparison | Compare two networks descriptively |
| plot.net_edge_betweenness | Edge Betweenness Network |
| plot.net_entropy_bayes | Bayesian Transition Entropy |
| plot.net_entropy_trajectory | Sliding-Window Transition Entropy Trajectory |
| plot.net_honem | Build HONEM Embeddings for Higher-Order Networks |
| plot.net_hypergraph_cluster | Spectral clustering of hypergraph vertices |
| plot.net_hypergraph_transduction | Transductive label spreading on a hypergraph |
| plot.net_markov_order | Test the Markov order of a sequential process |
| plot.net_markov_stability | Markov Stability Analysis |
| plot.net_mmm | Fit a Mixed Markov Model |
| plot.net_mmm_clustering | Fit a Mixed Markov Model |
| plot.net_mogen | Build Multi-Order Generative Model (MOGen) |
| plot.net_mpt | Mean First Passage Times |
| plot.net_network_comparison | Compare two or more networks |
| plot.net_outcome_model | Model Unit-Level Outcomes from Sequence or Network Predictors |
| plot.net_path_dependence | Per-Context Path Dependence at Order k |
| plot.net_reliability | Split-Half Reliability for Network Estimates |
| plot.net_sequence_comparison | Compare Subsequence Patterns Between Groups |
| plot.net_stability | Centrality Stability Coefficient (CS-coefficient) |
| plot.net_transition_entropy | Transition Entropy of a Markov Chain |
| plot.net_vertex_bootstrap | Vertex Bootstrap for Network-Level Statistics |
| plot.net_vertex_comparison | Compare Network-Level Statistics of Two Networks |
| plot.pc_loading_stability | Composite-Weight Stability Under Case Resampling |
| plot.persistence_landscape | Persistence Landscape |
| plot.persistent_homology | Persistent Homology |
| plot.q_analysis | Q-Analysis |
| plot.simplicial_complex | Build a Simplicial Complex |
| plot.state_freq | Plot State Frequency Distributions |
| plot_mosaic | Draw a Marimekko / Mosaic Plot from a Tidy Data Frame |
| plot_state_frequencies | Plot State Frequency Distributions |
| plot_state_frequencies.default | Plot State Frequency Distributions |
| plot_state_frequencies.htna | Plot State Frequency Distributions |
| plot_state_frequencies.mcml | Plot State Frequency Distributions |
| plot_state_frequencies.netobject | Plot State Frequency Distributions |
| plot_state_frequencies.netobject_group | Plot State Frequency Distributions |
| predictability | Compute Node Predictability |
| predictability.netobject | Compute Node Predictability |
| predictability.netobject_group | Compute Node Predictability |
| predictability.netobject_ml | Compute Node Predictability |
| predict_links | Predict Missing or Future Links in a Network |
| prepare | Prepare Event Log Data for Network Estimation |
| prepare_for_tna | Prepare Data for TNA Analysis |
| prepare_onehot | Import One-Hot Encoded Data into Sequence Format |
| print.boot_glasso | Bootstrap for Regularized Partial Correlation Networks |
| print.chain_structure | Qualitative structure of a discrete-time Markov chain |
| print.chain_structure_group | Qualitative structure of a discrete-time Markov chain |
| print.cluster_choice | Cluster Choice - sweep k, dissimilarity and method |
| print.hypergraph_measures | Structural measures for a hypergraph |
| print.magnitude_difference | Magnitude difference between the frequency and probability views |
| print.mcml | Build MCML from Raw Transition Data |
| print.mcml_layer | Build MCML from Raw Transition Data |
| print.mcml_pc | Multi-Cluster Multi-Level Aggregation for Psychometric Networks |
| print.mcml_sequence_plot | Sequence Plot (heatmap, index, or distribution) |
| print.mmm_compare | Compare MMM fits across different k |
| print.mosaic_analysis | Two-variable mosaic analysis (chi-square test + flat mosaic) |
| print.nestimate_data | Prepare Event Log Data for Network Estimation |
| print.nestimate_facet_list | Plot State Frequency Distributions |
| print.nestimate_facet_plot | Plot State Frequency Distributions |
| print.netdifference | Subtract one network from another |
| print.netobject | Build a Network |
| print.netobject_group | Build a Network |
| print.netobject_ml | Build a Network |
| print.net_association_rules | Discover Association Rules from Sequential or Transaction Data |
| print.net_bayes | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| print.net_bayes_group | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| print.net_bootstrap | Bootstrap a Network Estimate |
| print.net_bootstrap_group | Bootstrap a Network Estimate |
| print.net_casedrop_reliability | Edge-weight Case-dropping Stability |
| print.net_casedrop_reliability_group | Edge-weight Case-dropping Stability |
| print.net_certainty | Analytic certainty of network edges (Bayesian Dirichlet-Multinomial) |
| print.net_clustering | Cluster Sequences by Dissimilarity |
| print.net_cluster_diagnostics | Cluster Diagnostics |
| print.net_comparison | Compare two networks descriptively |
| print.net_entropy_bayes | Bayesian Transition Entropy |
| print.net_entropy_bayes_group | Bayesian Transition Entropy |
| print.net_entropy_trajectory | Sliding-Window Transition Entropy Trajectory |
| print.net_hon | Build a Higher-Order Network (HON) |
| print.net_honem | Build HONEM Embeddings for Higher-Order Networks |
| print.net_hypa | Detect Path Anomalies via HYPA |
| print.net_hypergraph | Higher-order hypergraph from a network's clique structure |
| print.net_hypergraph_cluster | Spectral clustering of hypergraph vertices |
| print.net_hypergraph_transduction | Transductive label spreading on a hypergraph |
| print.net_link_prediction | Predict Missing or Future Links in a Network |
| print.net_markov_order | Test the Markov order of a sequential process |
| print.net_markov_order_group | Test the Markov order of a sequential process |
| print.net_markov_stability | Markov Stability Analysis |
| print.net_markov_stability_group | Markov Stability Analysis |
| print.net_mlvar | Build a Multilevel Vector Autoregression (mlVAR) network |
| print.net_mmm | Fit a Mixed Markov Model |
| print.net_mmm_clustering | Fit a Mixed Markov Model |
| print.net_mogen | Build Multi-Order Generative Model (MOGen) |
| print.net_mpt | Mean First Passage Times |
| print.net_mpt_group | Mean First Passage Times |
| print.net_nct | Network Comparison Test |
| print.net_network_comparison | Compare two or more networks |
| print.net_outcome_model | Model Unit-Level Outcomes from Sequence or Network Predictors |
| print.net_path_dependence | Per-Context Path Dependence at Order k |
| print.net_permutation | Permutation Test for Network Comparison |
| print.net_permutation_group | Permutation Test for Network Comparison |
| print.net_pruning_details | Report Network Pruning Details |
| print.net_reliability | Split-Half Reliability for Network Estimates |
| print.net_sequence_comparison | Compare Subsequence Patterns Between Groups |
| print.net_stability | Centrality Stability Coefficient (CS-coefficient) |
| print.net_stability_group | Centrality Stability Coefficient (CS-coefficient) |
| print.net_table | Tables of a network comparison |
| print.net_transition_entropy | Transition Entropy of a Markov Chain |
| print.net_transition_entropy_group | Transition Entropy of a Markov Chain |
| print.net_vertex_bootstrap | Vertex Bootstrap for Network-Level Statistics |
| print.net_vertex_comparison | Compare Network-Level Statistics of Two Networks |
| print.pc_loading_stability | Composite-Weight Stability Under Case Resampling |
| print.persistence_landscape | Persistence Landscape |
| print.persistent_homology | Persistent Homology |
| print.q_analysis | Q-Analysis |
| print.simplicial_complex | Build a Simplicial Complex |
| print.state_freq | Plot State Frequency Distributions |
| print.summary.netobject | Build a Network |
| print.summary.netobject_group | Build a Network |
| print.summary.net_casedrop_reliability_group | Edge-weight Case-dropping Stability |
| print.summary.net_mpt | Mean First Passage Times |
| print.summary.net_path_dependence | Per-Context Path Dependence at Order k |
| print.summary.net_transition_entropy | Transition Entropy of a Markov Chain |
| print.summary_chain_structure | Qualitative structure of a discrete-time Markov chain |
| print.tidy_covariates | Cluster Sequences by Dissimilarity |
| print.wtna_boot_mixed | Bootstrap a Network Estimate |
| print.wtna_mixed | Window-based Transition Network Analysis |
| print.wtna_perm_mixed | Permutation Test for Network Comparison |
| q_analysis | Q-Analysis |
| register_estimator | Register a Network Estimator |
| remove_estimator | Remove a Registered Estimator |
| rename_models | Rename the models of a 'netobject_group' |
| rename_models.default | Rename the models of a 'netobject_group' |
| rename_models.netobject_group | Rename the models of a 'netobject_group' |
| sequence_compare | Compare Subsequence Patterns Between Groups |
| sequence_plot | Sequence Plot (heatmap, index, or distribution) |
| session_ids | The session behind each sequence |
| session_ids.default | The session behind each sequence |
| session_ids.netobject | The session behind each sequence |
| session_ids.net_clustering | The session behind each sequence |
| session_ids.net_mmm | The session behind each sequence |
| set_state_colors | Set the state colours carried by a network object |
| set_state_colors.default | Set the state colours carried by a network object |
| set_state_colors.htna | Set the state colours carried by a network object |
| set_state_colors.mcml | Set the state colours carried by a network object |
| set_state_colors.netobject | Set the state colours carried by a network object |
| set_state_colors.netobject_group | Set the state colours carried by a network object |
| simplicial_degree | Simplicial Degree |
| simplicial_features | Tidy Topological Features for One or Many Networks |
| srl_strategies | Self-Regulated Learning Strategy Frequencies |
| state_colors | The state colours an object will draw with |
| state_colors.default | The state colours an object will draw with |
| state_colors.htna | The state colours an object will draw with |
| state_colors.mcml | The state colours an object will draw with |
| state_colors.netobject | The state colours an object will draw with |
| state_colors.netobject_group | The state colours an object will draw with |
| state_colors<- | Set the state colours carried by a network object |
| state_distribution | Per-Class State Distribution as a Tidy Data Frame |
| state_distribution.default | Per-Class State Distribution as a Tidy Data Frame |
| state_distribution.htna | Per-Class State Distribution as a Tidy Data Frame |
| state_distribution.mcml | Per-Class State Distribution as a Tidy Data Frame |
| state_distribution.netobject | Per-Class State Distribution as a Tidy Data Frame |
| state_distribution.netobject_group | Per-Class State Distribution as a Tidy Data Frame |
| state_freq | Plot State Frequency Distributions |
| state_frequencies | Compute State Frequencies from Trajectory Data |
| subtract_networks | Subtract one network from another |
| summary.boot_glasso | Bootstrap for Regularized Partial Correlation Networks |
| summary.chain_structure | Qualitative structure of a discrete-time Markov chain |
| summary.chain_structure_group | Qualitative structure of a discrete-time Markov chain |
| summary.cluster_choice | Cluster Choice - sweep k, dissimilarity and method |
| summary.mcml | Build MCML from Raw Transition Data |
| summary.mcml_pc | Multi-Cluster Multi-Level Aggregation for Psychometric Networks |
| summary.mmm_compare | Compare MMM fits across different k |
| summary.mosaic_analysis | Two-variable mosaic analysis (chi-square test + flat mosaic) |
| summary.nest_initial_probs | Extract Initial Probabilities from Model |
| summary.nest_transition_counts | Build a Transition Frequency Matrix |
| summary.nest_transition_matrix | Extract Transition Matrix from Model |
| summary.netobject | Build a Network |
| summary.netobject_group | Build a Network |
| summary.net_association_rules | Discover Association Rules from Sequential or Transaction Data |
| summary.net_bayes | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| summary.net_bayes_group | Bayesian Dirichlet-Multinomial comparison of two transition networks |
| summary.net_bootstrap | Bootstrap a Network Estimate |
| summary.net_bootstrap_group | Bootstrap a Network Estimate |
| summary.net_casedrop_reliability | Edge-weight Case-dropping Stability |
| summary.net_casedrop_reliability_group | Edge-weight Case-dropping Stability |
| summary.net_clustering | Cluster Sequences by Dissimilarity |
| summary.net_entropy_bayes | Bayesian Transition Entropy |
| summary.net_entropy_trajectory | Sliding-Window Transition Entropy Trajectory |
| summary.net_hon | Build a Higher-Order Network (HON) |
| summary.net_honem | Build HONEM Embeddings for Higher-Order Networks |
| summary.net_hypa | Detect Path Anomalies via HYPA |
| summary.net_hypergraph | Higher-order hypergraph from a network's clique structure |
| summary.net_hypergraph_cluster | Spectral clustering of hypergraph vertices |
| summary.net_hypergraph_transduction | Transductive label spreading on a hypergraph |
| summary.net_link_prediction | Predict Missing or Future Links in a Network |
| summary.net_markov_order | Test the Markov order of a sequential process |
| summary.net_markov_stability | Markov Stability Analysis |
| summary.net_mlvar | Build a Multilevel Vector Autoregression (mlVAR) network |
| summary.net_mmm | Fit a Mixed Markov Model |
| summary.net_mogen | Build Multi-Order Generative Model (MOGen) |
| summary.net_mpt | Mean First Passage Times |
| summary.net_nct | Network Comparison Test |
| summary.net_network_comparison | Tables of a network comparison |
| summary.net_outcome_model | Model Unit-Level Outcomes from Sequence or Network Predictors |
| summary.net_path_dependence | Per-Context Path Dependence at Order k |
| summary.net_permutation | Permutation Test for Network Comparison |
| summary.net_permutation_group | Permutation Test for Network Comparison |
| summary.net_reliability | Split-Half Reliability for Network Estimates |
| summary.net_sequence_comparison | Compare Subsequence Patterns Between Groups |
| summary.net_stability | Centrality Stability Coefficient (CS-coefficient) |
| summary.net_stability_group | Centrality Stability Coefficient (CS-coefficient) |
| summary.net_transition_entropy | Transition Entropy of a Markov Chain |
| summary.net_vertex_bootstrap | Vertex Bootstrap for Network-Level Statistics |
| summary.net_vertex_comparison | Compare Network-Level Statistics of Two Networks |
| summary.wtna_boot_mixed | Bootstrap a Network Estimate |
| summary.wtna_perm_mixed | Permutation Test for Network Comparison |
| trajectories | Student Engagement Trajectories |
| transition_entropy | Transition Entropy of a Markov Chain |
| validate_netobject | Validate a netobject / cograph_network against the shared schema |
| verify_simplicial | Verify Simplicial Complex Against igraph |
| vertex_bootstrap | Vertex Bootstrap for Network-Level Statistics |
| vertex_compare | Compare Network-Level Statistics of Two Networks |
| wide_to_long | Convert Wide Sequences to Long Format |
| wtna | Window-based Transition Network Analysis |