The entropy of a transition network | Which states are decision points? | The entropy network | Entropy over time | Bayesian estimation | When to use which | References
Package: Nestimate 0.9.26
Mohammed Saqr
Nestimate: Dynamic, Probabilistic, and Higher-Order Network Analysis
Estimate, compare, and analyze dynamic and psychological networks using a unified interface. Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) <doi:10.1145/3706468.3706513>, psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) <doi:10.1007/978-3-031-54464-4_19>, and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering.
Authors:
Nestimate_0.9.26.tar.gz
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Nestimate_0.9.26.tgz(r-4.6-any)Nestimate_0.9.26.tgz(r-4.5-any)
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Nestimate_0.9.26.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Nestimate/json (API)
| # Install 'Nestimate' in R: |
| install.packages('Nestimate', repos = c('https://mohsaqr.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mohsaqr/nestimate/issues
Pkgdown/docs site:https://pak.dynasite.org
Datasets:
- ai_long - Human-AI Vibe Coding Interaction Data
- chatgpt_srl - ChatGPT Self-Regulated Learning Scale Scores
- group_regulation_long - Group Regulation in Collaborative Learning
- human_long - Human-AI Vibe Coding Interaction Data
- learning_activities - Online Learning Activity Indicators
- srl_strategies - Self-Regulated Learning Strategy Frequencies
- trajectories - Student Engagement Trajectories
Last updated from:57941e9406. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 239 | ||
| source / vignettes | OK | 308 | ||
| linux-release | OK | 226 | ||
| macos-release | OK | 232 | ||
| macos-oldrel | OK | 172 | ||
| windows-devel | OK | 162 | ||
| windows-release | OK | 178 | ||
| windows-oldrel | OK | 128 | ||
| wasm-release | OK | 136 |
Exports:action_to_onehotactor_endpointsas_htnaas_netdifferenceas_netobjectas_networksas_tnaassociation_rulesbayes_comparebetti_numbersbipartite_groupsboot_glassobootstrap_networkbottleneck_distancebuild_atnabuild_clustersbuild_cnabuild_corbuild_ftnabuild_gimmebuild_glassobuild_honbuild_honembuild_hypabuild_hypergraphbuild_isingbuild_mcmlbuild_mcml_pcbuild_mlvarbuild_mmmbuild_mogenbuild_networkbuild_pcorbuild_simplicialbuild_tnacasedrop_reliabilitycentrality_stabilitycertaintychain_structureclique_expansioncluster_choicecluster_datacluster_diagnosticscluster_mmmcluster_networkcluster_summarycoefscompare_mmmcompare_modelcompare_networkscompositesconvert_sequence_formatcooccurrencedistribution_plotedge_differenceseffects_tableentropy_bayesentropy_networkentropy_trajectoryestimate_networkeuler_characteristicevaluate_linksextract_edgesextract_initial_probsextract_pathwaysextract_transition_matrixfrequenciesget_estimatorglobal_differenceshypergraph_centralityhypergraph_clusterhypergraph_laplacianhypergraph_measureshypergraph_transductionitem_loadingslist_estimatorsloading_stabilitylong_to_widemacro_networkmagnitude_differencemark_first_statemark_terminal_statemarkov_order_testmarkov_stabilitymogen_transitionsmosaic_analysismosaic_plotnctnet_aggregate_weightsnet_centralitynet_deprunenet_edge_betweennessnet_prunenet_pruning_detailsnet_reprunenetwork_metricsnetwork_reliabilitynode_differencesoutcome_modelpassage_timepath_countspath_dependencepathwayspermutationpermutation_diagnosticspersistence_landscapepersistent_homologyplot_mosaicplot_state_frequenciespredict_linkspredictabilityprepareprepare_for_tnaprepare_onehotq_analysisregister_estimatorremove_estimatorrename_modelssequence_comparesequence_plotsession_idsset_state_colorssimplicial_degreesimplicial_featuresstate_colorsstate_colors<-state_distributionstate_frequenciessubtract_networkstransition_entropyvalidate_netobjectverify_simplicialvertex_bootstrapvertex_comparewide_to_longwtna
Dependencies:brglm2cliclustercpp11data.tableenrichwithfarverggplot2gluegtableidiographicisobandlabelinglatticelifecycleMASSMatrixnleqslvnnetnumDerivpsychnetsR6RColorBrewerrlangS7scalesstatmodvctrsviridisLitewithr
Transition Matrix Entropy
Last update: 2026-08-21
Started: 2026-08-21
Sequence Clustering
Introduction | Data | Dissimilarity-based Clustering | Visualizing Clusters | Distance Metrics | Clustering Methods | Choosing k, dissimilarity, and method | Validating the choice with cluster_diagnostics() | Mixture Markov Models | Building Networks per Cluster | From build_clusters() to per-cluster networks | Shortcut: cluster_network() (distance-based) | Fitted MMM clustering and its networks | Comparing Clusters | Workflow Summary
Last update: 2026-07-21
Started: 2026-03-27
Network Estimation and Analysis with Nestimate
Part I: Transition Network Analysis | Theoretical Grounding | Data | Building Networks | Transition Network (TNA) | Frequency Network (FTNA) | Attention Network (ATNA) | Co-occurrence Network from Binary Data | Window-based TNA (WTNA) | Mixed Network (Transitions + Co-occurrences) | Validation | Reliability | Bootstrap Analysis | Centrality Stability | Clustering | Permutation Test for Clusters | Post-hoc Covariate Analysis | Part II: Psychological Network Analysis | Regularized Network (EBICglasso) | References
Last update: 2026-05-22
Started: 2026-03-25
Markov Stability Analysis
Data | Selecting mostly-active learners | Sequence plots | Transition networks | Mean First Passage Times | Stationary distribution | Markov Stability
Last update: 2026-05-20
Started: 2026-04-18
Sequence Pattern Comparison: Early vs Late Human-AI Interactions
1. The dataset | 2. Split by time — early vs late interactions | 3. Build the grouped network | 4. Compare patterns between early and late | How to read the residuals | 5. Pyramid plot | 6. Heatmap | 7. Sort by frequency | 9. Note on the test choice
Last update: 2026-05-20
Started: 2026-04-18
Sequence Plots: heatmap, index, and distribution
The dataset | 1. type = "heatmap" — clustered carpet | 1.1 Default — LCS distance, ward.D2 dendrogram | 1.2 Switch the sort strategy | 1.3 Cluster separators with k | 1.4 Legend position, custom palette, title | 1.5 Cell borders + tick thinning | 1.6 frame = TRUE brings back the outer box | 2. type = "index" — gap-ready carpet with facets | 2.1 Single panel | 2.2 Visible row gaps | 2.3 Faceted by net_clustering (auto 2×2 for k = 3) | 2.4 Force a 1×3 row | 3. type = "distribution" — state proportions over time | 3.1 Default stacked area | 3.2 Stacked bars, count scale | 3.3 NA band on/off | 3.4 Faceted by cluster | Cheat sheet
Last update: 2026-05-20
Started: 2026-04-18
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert Action Column to One-Hot Encoding | action_to_onehot |
| Tidy per-actor endpoint summary of a wide-format sequence dataset | actor_endpoints |
| Build a grouped node-level network (htna) from data and a clustering | as_htna as_htna.default as_htna.mcml as_htna.net_mmm |
| Coerce an inferential comparison to a network difference | as_netdifference as_netdifference.default as_netdifference.netdifference as_netdifference.net_bayes |
| Coerce a network object to a Nestimate netobject | as_netobject as_netobject.cograph_network as_netobject.default as_netobject.netobject as_netobject.psychnet |
| Promote a psychometric MCML result to a network group | as_networks as_networks.default as_networks.mcml_pc |
| Promote the Layers of an mcml to Networks | as_tna as_tna.default as_tna.mcml |
| Discover Association Rules from Sequential or Transaction Data | association_rules plot.net_association_rules print.net_association_rules summary.net_association_rules |
| Bayesian Dirichlet-Multinomial comparison of two transition networks | bayes_compare plot.net_bayes print.net_bayes print.net_bayes_group summary.net_bayes summary.net_bayes_group |
| Betti Numbers | betti_numbers |
| Hypergraph from bipartite group / event data | bipartite_groups |
| Bootstrap for Regularized Partial Correlation Networks | boot_glasso plot.boot_glasso print.boot_glasso summary.boot_glasso |
| Bootstrap a Network Estimate | bootstrap_network print.net_bootstrap print.net_bootstrap_group print.wtna_boot_mixed summary.net_bootstrap summary.net_bootstrap_group summary.wtna_boot_mixed |
| Bottleneck Distance Between Persistence Diagrams | bottleneck_distance |
| Build an Attention-Weighted Transition Network (ATNA) | build_atna |
| Cluster Sequences by Dissimilarity | build_clusters plot.net_clustering print.net_clustering print.tidy_covariates summary.net_clustering |
| Build a Co-occurrence Network (CNA) | build_cna |
| Build a Correlation Network | build_cor |
| Build a Frequency Transition Network (FTNA) | build_ftna |
| GIMME: Group Iterative Multiple Model Estimation | build_gimme |
| Build a Graphical Lasso Network (EBICglasso) | build_glasso |
| Build a Higher-Order Network (HON) | build_hon print.net_hon summary.net_hon |
| Build HONEM Embeddings for Higher-Order Networks | build_honem plot.net_honem print.net_honem summary.net_honem |
| Detect Path Anomalies via HYPA | build_hypa print.net_hypa summary.net_hypa |
| Higher-order hypergraph from a network's clique structure | build_hypergraph print.net_hypergraph summary.net_hypergraph |
| Build an Ising Network | build_ising |
| Build MCML from Raw Transition Data | build_mcml print.mcml print.mcml_layer summary.mcml |
| Multi-Cluster Multi-Level Aggregation for Psychometric Networks | build_mcml_pc plot.mcml_pc print.mcml_pc summary.mcml_pc |
| Build a Multilevel Vector Autoregression (mlVAR) network | build_mlvar print.net_mlvar summary.net_mlvar |
| Fit a Mixed Markov Model | build_mmm plot.net_mmm plot.net_mmm_clustering print.net_mmm print.net_mmm_clustering summary.net_mmm |
| Build Multi-Order Generative Model (MOGen) | build_mogen plot.net_mogen print.net_mogen summary.net_mogen |
| Build a Network | build_network print.netobject print.netobject_group print.netobject_ml print.summary.netobject print.summary.netobject_group summary.netobject summary.netobject_group |
| Build a Partial Correlation Network | build_pcor |
| Build a Simplicial Complex | build_simplicial plot.simplicial_complex print.simplicial_complex |
| Build a Transition Network (TNA) | build_tna |
| Edge-weight Case-dropping Stability | casedrop_reliability plot.net_casedrop_reliability plot.net_casedrop_reliability_group print.net_casedrop_reliability print.net_casedrop_reliability_group print.summary.net_casedrop_reliability_group summary.net_casedrop_reliability summary.net_casedrop_reliability_group |
| Centrality Stability Coefficient (CS-coefficient) | centrality_stability plot.net_stability print.net_stability print.net_stability_group summary.net_stability summary.net_stability_group |
| Analytic certainty of network edges (Bayesian Dirichlet-Multinomial) | certainty print.net_certainty |
| Qualitative structure of a discrete-time Markov chain | chain_structure plot.chain_structure print.chain_structure print.chain_structure_group print.summary_chain_structure summary.chain_structure summary.chain_structure_group |
| ChatGPT Self-Regulated Learning Scale Scores | chatgpt_srl |
| Clique expansion of a hypergraph | clique_expansion |
| Cluster Choice - sweep k, dissimilarity and method | cluster_choice plot.cluster_choice print.cluster_choice summary.cluster_choice |
| Cluster Diagnostics | as.data.frame.net_cluster_diagnostics cluster_diagnostics plot.net_cluster_diagnostics print.net_cluster_diagnostics |
| Cluster sequences using Mixed Markov Models | cluster_mmm |
| Cluster data and build per-cluster networks in one step | cluster_network |
| Cluster Summary Statistics | cluster_summary |
| Tidy coefficients from a fitted mlvar model | coefs coefs.default coefs.net_mlvar |
| Compare MMM fits across different k | compare_mmm plot.mmm_compare print.mmm_compare summary.mmm_compare |
| Compare two networks descriptively | compare_model compare_model.cograph_network compare_model.matrix compare_model.netobject compare_model.netobject_group plot.net_comparison print.net_comparison |
| Compare two or more networks | compare_networks plot.net_network_comparison print.net_network_comparison |
| Tables of a network comparison | comparison_tables edge_differences global_differences network_metrics node_differences print.net_table summary.net_network_comparison |
| Cluster Scores From a Psychometric MCML Fit | composites composites.mcml_pc |
| Convert Sequence Data to Different Formats | convert_sequence_format |
| Build a Co-occurrence Network | cooccurrence |
| State Distribution Plot Over Time | distribution_plot |
| Effect Table of a Fitted Outcome Model | effects_table |
| Bayesian Transition Entropy | entropy_bayes plot.net_entropy_bayes print.net_entropy_bayes print.net_entropy_bayes_group summary.net_entropy_bayes |
| Transition Entropy Network | entropy_network |
| Sliding-Window Transition Entropy Trajectory | entropy_trajectory plot.net_entropy_trajectory print.net_entropy_trajectory summary.net_entropy_trajectory |
| Estimate a Network (Deprecated) | estimate_network |
| Euler Characteristic | euler_characteristic |
| Evaluate Link Predictions Against Known Edges | evaluate_links |
| Extract Edge List with Weights | extract_edges |
| Extract Initial Probabilities from Model | extract_initial_probs summary.nest_initial_probs |
| Cut an Event Log into Pathways | extract_pathways |
| Extract Transition Matrix from Model | extract_transition_matrix summary.nest_transition_matrix |
| Build a Transition Frequency Matrix | frequencies summary.nest_transition_counts |
| Retrieve a Registered Estimator | get_estimator |
| Group Regulation in Collaborative Learning (Long Format) | group_regulation_long |
| Hypergraph eigenvector centralities | hypergraph_centrality |
| Spectral clustering of hypergraph vertices | as.data.frame.net_hypergraph_cluster hypergraph_cluster plot.net_hypergraph_cluster print.net_hypergraph_cluster summary.net_hypergraph_cluster |
| Normalized hypergraph Laplacian | hypergraph_laplacian |
| Structural measures for a hypergraph | hypergraph_measures print.hypergraph_measures |
| Transductive label spreading on a hypergraph | as.data.frame.net_hypergraph_transduction hypergraph_transduction plot.net_hypergraph_transduction print.net_hypergraph_transduction summary.net_hypergraph_transduction |
| Item Diagnostics From a Psychometric MCML Fit | item_loadings item_loadings.mcml_pc |
| Online Learning Activity Indicators | learning_activities |
| List All Registered Estimators | list_estimators |
| Composite-Weight Stability Under Case Resampling | loading_stability plot.pc_loading_stability print.pc_loading_stability |
| Convert Long Format to Wide Sequences | long_to_wide |
| Human-AI Vibe Coding Interaction Data (Long Format) | ai_long human_long long-data |
| Cluster-Level Network, With One Cluster Expanded | macro_network |
| Magnitude difference between the frequency and probability views | magnitude_difference plot.magnitude_difference print.magnitude_difference |
| Mark leading-NA cells with an explicit state label | mark_first_state |
| Mark terminal-NA cells with an explicit state label | mark_terminal_state |
| Test the Markov order of a sequential process | markov_order_test plot.net_markov_order print.net_markov_order print.net_markov_order_group summary.net_markov_order |
| Markov Stability Analysis | markov_stability plot.net_markov_stability print.net_markov_stability print.net_markov_stability_group summary.net_markov_stability |
| Extract Transition Table from a MOGen Model | mogen_transitions |
| Two-variable mosaic analysis (chi-square test + flat mosaic) | mosaic_analysis plot.mosaic_analysis print.mosaic_analysis summary.mosaic_analysis |
| Mosaic Plot of a Network's Transition or Co-occurrence Counts | mosaic_plot mosaic_plot.default mosaic_plot.htna mosaic_plot.matrix mosaic_plot.mcml mosaic_plot.netobject mosaic_plot.netobject_group mosaic_plot.table |
| Network Comparison Test | nct print.net_nct summary.net_nct |
| Aggregate Edge Weights | net_aggregate_weights |
| Compute Centrality Measures for a Network | net_centrality plot.net_centrality plot.net_centrality_group |
| Undo Network Pruning | net_deprune net_deprune.default net_deprune.netobject net_deprune.netobject_group |
| Edge Betweenness Network | net_edge_betweenness net_edge_betweenness.default net_edge_betweenness.netobject net_edge_betweenness.netobject_group plot.net_edge_betweenness |
| Prune a Network's Edges | net_prune net_prune.default net_prune.netobject net_prune.netobject_group |
| Report Network Pruning Details | net_pruning_details net_pruning_details.default net_pruning_details.netobject net_pruning_details.netobject_group print.net_pruning_details |
| Re-apply Network Pruning | net_reprune net_reprune.default net_reprune.netobject net_reprune.netobject_group |
| Split-Half Reliability for Network Estimates | network_reliability plot.net_reliability print.net_reliability summary.net_reliability |
| Model Unit-Level Outcomes from Sequence or Network Predictors | outcome_model plot.net_outcome_model print.net_outcome_model summary.net_outcome_model |
| Mean First Passage Times | passage_time plot.net_mpt print.net_mpt print.net_mpt_group print.summary.net_mpt summary.net_mpt |
| Count Path Frequencies in Trajectory Data | path_counts |
| Per-Context Path Dependence at Order k | path_dependence plot.net_path_dependence print.net_path_dependence print.summary.net_path_dependence summary.net_path_dependence |
| Extract Pathways from Higher-Order Network Objects | pathways pathways.netobject pathways.net_association_rules pathways.net_hon pathways.net_hypa pathways.net_link_prediction pathways.net_mogen |
| Permutation Test for Network Comparison | permutation print.net_permutation print.net_permutation_group print.wtna_perm_mixed summary.net_permutation summary.net_permutation_group summary.wtna_perm_mixed |
| Does Nesting Bias a Permutation Test? | permutation_diagnostics |
| Persistence Landscape | persistence_landscape plot.persistence_landscape print.persistence_landscape |
| Persistent Homology | persistent_homology plot.persistent_homology print.persistent_homology |
| Draw a Marimekko / Mosaic Plot from a Tidy Data Frame | plot_mosaic |
| Plot State Frequency Distributions | as.data.frame.state_freq plot.state_freq plot_state_frequencies plot_state_frequencies.default plot_state_frequencies.htna plot_state_frequencies.mcml plot_state_frequencies.netobject plot_state_frequencies.netobject_group print.nestimate_facet_list print.nestimate_facet_plot print.state_freq state_freq |
| Predict Missing or Future Links in a Network | predict_links print.net_link_prediction summary.net_link_prediction |
| Compute Node Predictability | predictability predictability.netobject predictability.netobject_group predictability.netobject_ml |
| Prepare Event Log Data for Network Estimation | prepare print.nestimate_data |
| Prepare Data for TNA Analysis | prepare_for_tna |
| Import One-Hot Encoded Data into Sequence Format | prepare_onehot |
| Q-Analysis | plot.q_analysis print.q_analysis q_analysis |
| Register a Network Estimator | register_estimator |
| Remove a Registered Estimator | remove_estimator |
| Rename the models of a 'netobject_group' | rename_models rename_models.default rename_models.netobject_group |
| Compare Subsequence Patterns Between Groups | plot.net_sequence_comparison print.net_sequence_comparison sequence_compare summary.net_sequence_comparison |
| Sequence Plot (heatmap, index, or distribution) | print.mcml_sequence_plot sequence_plot |
| The session behind each sequence | session_ids session_ids.default session_ids.netobject session_ids.net_clustering session_ids.net_mmm |
| Set the state colours carried by a network object | set_state_colors set_state_colors.default set_state_colors.htna set_state_colors.mcml set_state_colors.netobject set_state_colors.netobject_group state_colors<- |
| Simplicial Degree | simplicial_degree |
| Tidy Topological Features for One or Many Networks | simplicial_features |
| Self-Regulated Learning Strategy Frequencies | srl_strategies |
| The state colours an object will draw with | state_colors state_colors.default state_colors.htna state_colors.mcml state_colors.netobject state_colors.netobject_group |
| Per-Class State Distribution as a Tidy Data Frame | state_distribution state_distribution.default state_distribution.htna state_distribution.mcml state_distribution.netobject state_distribution.netobject_group |
| Compute State Frequencies from Trajectory Data | state_frequencies |
| Subtract one network from another | print.netdifference subtract_networks |
| Student Engagement Trajectories | trajectories |
| Transition Entropy of a Markov Chain | plot.net_transition_entropy print.net_transition_entropy print.net_transition_entropy_group print.summary.net_transition_entropy summary.net_transition_entropy transition_entropy |
| Validate a netobject / cograph_network against the shared schema | validate_netobject |
| Verify Simplicial Complex Against igraph | verify_simplicial |
| Vertex Bootstrap for Network-Level Statistics | plot.net_vertex_bootstrap print.net_vertex_bootstrap summary.net_vertex_bootstrap vertex_bootstrap |
| Compare Network-Level Statistics of Two Networks | plot.net_vertex_comparison print.net_vertex_comparison summary.net_vertex_comparison vertex_compare |
| Convert Wide Sequences to Long Format | wide_to_long |
| Window-based Transition Network Analysis | print.wtna_mixed wtna |