{ "_id": "6a20a3adcd65a98ecbd17dc9", "Package": "Nestimate", "Title": "Network Estimation, Bootstrap, and Higher-Order Analysis", "Version": "0.6.2", "Authors@R": "c(\nperson(\"Mohammed\", \"Saqr\", email = \"saqr@saqr.me\", role = c(\"aut\", \"cre\", \"cph\")),\nperson(\"Sonsoles\", \"López-Pernas\", email = \"sonsoles.lopez@uef.fi\", role = \"aut\"),\nperson(\"Kamila\", \"Misiejuk\", email = \"kamila.misiejuk@fernuni-hagen.de\", role = \"aut\")\n)", "Description": "Estimate, compare, and analyze dynamic and psychological\nnetworks using a unified interface. Provides transition network\nanalysis estimation (transition, frequency, co-occurrence,\nattention-weighted) Saqr et al. (2025)\n, psychological network methods\n(correlation, partial correlation, 'graphical lasso', 'Ising')\nSaqr, Beck, and Lopez-Pernas (2024)\n, and higher-order network\nmethods including higher-order networks, higher-order network\nembedding, hyper-path anomaly, and multi-order generative\nmodel. Supports bootstrap inference, permutation testing,\nsplit-half reliability, centrality stability analysis, mixed\nMarkov models, multi-cluster multi-layer networks and\nclustering.", "License": "MIT + file LICENSE", "URL": "https://github.com/mohsaqr/Nestimate, https://saqr.me/Nestimate/", "BugReports": "https://github.com/mohsaqr/Nestimate/issues", "Language": "en-US", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "LazyData": "true", "VignetteBuilder": "knitr", "Repository": "https://mohsaqr.r-universe.dev", "Date/Publication": "2026-06-03 19:27:10 UTC", "RemoteUrl": "https://github.com/mohsaqr/Nestimate", "RemoteRef": "HEAD", "RemoteSha": "61d0906d3f14e07f1af02cf0f9f354467f085f53", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 21:52:17 UTC", "User": "root" }, "Author": "Mohammed Saqr [aut, cre, cph],\nSonsoles López-Pernas [aut],\nKamila Misiejuk [aut]", "Maintainer": "Mohammed Saqr ", "MD5sum": "aab27af73e0489bab16f1039b3f342a8", "_user": "mohsaqr", "_type": "src", "_file": "Nestimate_0.6.2.tar.gz", "_fileid": "b3946a203c45f83fa7dbe8fc7cd3dd36762b4e567d096760dcfdfaa4c230b808", "_filesize": 4760499, "_sha256": "b3946a203c45f83fa7dbe8fc7cd3dd36762b4e567d096760dcfdfaa4c230b808", "_created": "2026-06-03T21:52:17.000Z", "_published": "2026-06-03T21:59:09.261Z", "_distro": "noble", "_jobs": [ { "job": 79403527026, "time": 327, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7397711133" }, { "job": 79403527060, "time": 333, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7397712831" }, { "job": 79403527035, "time": 227, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7397680465" }, { "job": 79403527041, "time": 262, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7397691421" }, { "job": 79402803839, "time": 275, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7397610908" }, { "job": 79403527005, "time": 108, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7397644627" }, { "job": 79403527030, "time": 345, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7397716736" }, { "job": 79403527040, "time": 296, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7397702185" }, { "job": 79403527168, "time": 336, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7397714602" } ], "_buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/26915093232", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/mohsaqr/Nestimate", "_commit": { "id": "61d0906d3f14e07f1af02cf0f9f354467f085f53", "author": "mohsaqr ", "committer": "mohsaqr ", "message": "test: fix stale rank-scaling assertions (v0.6.2)\n\nThe scaling=\"rank\" test expected the old rank-all-cells behaviour (max\nrank 9) and asserted a value on the diagonal. .apply_scaling's rank branch\nintentionally preserves structural zeros and ranks only non-zero edges\n(rank(c(1,1,2,1)) = c(2,2,4,2)); the diagonal stays 0. Rewrote the\nassertions to match. Not a code change — the earlier \"rank returns raw\ncounts\" report was a stale test, now corrected.\n", "time": 1780514830 }, "_maintainer": { "name": "Mohammed Saqr", "email": "saqr@saqr.me", "login": "loxavian", "description": "", "uuid": 52792889 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "ggplot2", "role": "Imports" }, { "package": "glasso", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "cluster", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "brglm2", "role": "Imports" }, { "package": "nnet", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "igraph", "role": "Suggests" }, { "package": "glmnet", "role": "Suggests" }, { "package": "lavaan", "role": "Suggests" }, { "package": "stringdist", "role": "Suggests" }, { "package": "gridExtra", "role": "Suggests" }, { "package": "lme4", "role": 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"name": "Mohammed Saqr", "description": "Professor of Computer Science" }, "_downloads": { "count": 610, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/Nestimate" }, "_devurl": "https://github.com/mohsaqr/nestimate", "_pkgdown": "https://saqr.me/Nestimate/", "_searchresults": 27, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/Nestimate.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/mohsaqr/nestimate", "_realowner": "mohsaqr", "_cranurl": true, "_releases": [ { "version": "0.3.0", "date": "2026-04-08" }, { "version": "0.4.3", "date": "2026-04-20" }, { "version": "0.6.0", "date": "2026-05-31" } ], "_exports": [ "action_to_onehot", "actor_endpoints", "as_tna", "association_rules", "betti_numbers", "bipartite_groups", "boot_glasso", "bootstrap_network", "bottleneck_distance", 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"nct", "net_aggregate_weights", "net_centrality", "network_reliability", "passage_time", "path_counts", "path_dependence", "pathways", "permutation", "persistence_landscape", "persistent_homology", "plot_mosaic", "plot_state_frequencies", "predict_links", "predictability", "prepare", "prepare_for_tna", "prepare_onehot", "q_analysis", "register_estimator", "remove_estimator", "rename_models", "sequence_compare", "sequence_plot", "simplicial_degree", "state_distribution", "state_frequencies", "transition_entropy", "verify_simplicial", "wide_to_long", "wtna" ], "_datasets": [ { "name": "ai_long", "title": "Human-AI Vibe Coding Interaction Data (Long Format)", "object": "ai_long", "class": [ "data.frame" ], "fields": [ "message_id", "project", "session_id", "timestamp", "session_date", "code", "cluster", "code_order", "order_in_session" ], "rows": 8551, "table": true, "tojson": true }, { "name": "chatgpt_srl", "title": "ChatGPT Self-Regulated Learning Scale Scores", "object": 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"tojson": true }, { "name": "srl_strategies", "title": "Self-Regulated Learning Strategy Frequencies", "object": "srl_strategies", "class": [ "data.frame" ], "fields": [ "Planning", "Monitoring", "Evaluating", "Elaboration", "Organization", "Rehearsal", "Help_Seeking", "Time_Mgmt", "Effort_Reg" ], "rows": 250, "table": true, "tojson": true }, { "name": "trajectories", "title": "Student Engagement Trajectories", "object": "trajectories", "class": [ "matrix", "array" ], "fields": [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15" ], "rows": 138, "table": true, "tojson": true } ], "_help": [ { "page": "action_to_onehot", "title": "Convert Action Column to One-Hot Encoding", "topics": [ "action_to_onehot" ] }, { "page": "actor_endpoints", "title": "Tidy per-actor endpoint summary of a wide-format sequence dataset.", "topics": [ "actor_endpoints" ] }, { "page": "as_tna", "title": "Convert cluster_summary to tna Objects", "topics": [ "as_tna", "as_tna.default", "as_tna.mcml" ] }, { "page": "association_rules", "title": "Discover Association Rules from Sequential or Transaction Data", "topics": [ "association_rules" ] }, { "page": "betti_numbers", "title": "Betti Numbers", "topics": [ "betti_numbers" ] }, { "page": "bipartite_groups", "title": "Hypergraph from bipartite group / event data", "topics": [ "bipartite_groups" ] }, { "page": "boot_glasso", "title": "Bootstrap for Regularized Partial Correlation Networks", "topics": [ "boot_glasso" ] }, { "page": "bootstrap_network", "title": "Bootstrap a Network Estimate", "topics": [ "bootstrap_network" ] }, { "page": "bottleneck_distance", "title": "Bottleneck Distance Between Persistence Diagrams", "topics": [ "bottleneck_distance" ] }, { "page": "build_atna", "title": "Build an Attention-Weighted Transition Network (ATNA)", "topics": [ "build_atna" ] }, { "page": "build_clusters", "title": "Cluster Sequences by Dissimilarity", "topics": [ "build_clusters" ] }, { "page": "build_cna", "title": "Build a Co-occurrence Network (CNA)", "topics": [ "build_cna" ] }, { "page": "build_cor", "title": "Build a Correlation Network", "topics": [ "build_cor" ] }, { "page": "build_ftna", "title": "Build a Frequency Transition Network (FTNA)", "topics": [ "build_ftna" ] }, { "page": "build_gimme", "title": "GIMME: Group Iterative Multiple Model Estimation", "topics": [ "build_gimme" ] }, { "page": "build_glasso", "title": "Build a Graphical Lasso Network (EBICglasso)", "topics": [ "build_glasso" ] }, { "page": "build_hon", "title": "Build a Higher-Order Network (HON)", "topics": [ "build_hon" ] }, { "page": "build_honem", "title": "Build HONEM Embeddings for Higher-Order Networks", "topics": [ "build_honem" ] }, { "page": "build_hypa", "title": "Detect Path Anomalies via HYPA", "topics": [ "build_hypa" ] }, { "page": "build_hypergraph", "title": "Higher-order hypergraph from a network's clique structure", "topics": [ "build_hypergraph", "print.net_hypergraph", "summary.net_hypergraph" ] }, { "page": "build_ising", "title": "Build an Ising Network", "topics": [ "build_ising" ] }, { "page": "build_mcml", "title": "Build MCML from Raw Transition Data", "topics": [ "build_mcml" ] }, { "page": "build_mlvar", "title": "Build a Multilevel Vector Autoregression (mlVAR) network", "topics": [ "build_mlvar" ] }, { "page": "build_mmm", "title": "Fit a Mixed Markov Model", "topics": [ "build_mmm" ] }, { "page": "build_mogen", "title": "Build Multi-Order Generative Model (MOGen)", "topics": [ "build_mogen" ] }, { "page": "build_network", "title": "Build a Network", "topics": [ "build_network" ] }, { "page": "build_pcor", "title": "Build a Partial Correlation Network", "topics": [ "build_pcor" ] }, { "page": "build_simplicial", "title": "Build a Simplicial Complex", "topics": [ "build_simplicial" ] }, { "page": "build_tna", "title": "Build a Transition Network (TNA)", "topics": [ "build_tna" ] }, { "page": "casedrop_reliability", "title": "Edge-weight Case-dropping Stability", "topics": [ "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" ] }, { "page": "centrality_stability", "title": "Centrality Stability Coefficient (CS-coefficient)", "topics": [ "centrality_stability" ] }, { "page": "chain_structure", "title": "Qualitative structure of a discrete-time Markov chain.", "topics": [ "chain_structure" ] }, { "page": "chatgpt_srl", "title": "ChatGPT Self-Regulated Learning Scale Scores", "topics": [ "chatgpt_srl" ] }, { "page": "clique_expansion", "title": "Clique expansion of a hypergraph", "topics": [ "clique_expansion" ] }, { "page": "cluster_choice", "title": "Cluster Choice - sweep k, dissimilarity and method", "topics": [ "cluster_choice" ] }, { "page": "cluster_diagnostics", "title": "Cluster Diagnostics", "topics": [ "as.data.frame.net_cluster_diagnostics", "cluster_diagnostics" ] }, { "page": "cluster_mmm", "title": "Cluster sequences using Mixed Markov Models", "topics": [ "cluster_mmm" ] }, { "page": "cluster_network", "title": "Cluster data and build per-cluster networks in one step", "topics": [ "cluster_network" ] }, { "page": "cluster_summary", "title": "Cluster Summary Statistics", "topics": [ "cluster_summary" ] }, { "page": "coefs", "title": "Tidy coefficients from a fitted mlvar model", "topics": [ "coefs", "coefs.default", "coefs.net_mlvar" ] }, { "page": "compare_mmm", "title": "Compare MMM fits across different k", "topics": [ "compare_mmm" ] }, { "page": "compare_model", "title": "Compare two networks descriptively", "topics": [ "compare_model", "compare_model.cograph_network", "compare_model.matrix", "compare_model.netobject" ] }, { "page": "compare_model.netobject_group", "title": "Compare two networks within a netobject_group", "topics": [ "compare_model.netobject_group" ] }, { "page": "convert_sequence_format", "title": "Convert Sequence Data to Different Formats", "topics": [ "convert_sequence_format" ] }, { "page": "cooccurrence", "title": "Build a Co-occurrence Network", "topics": [ "cooccurrence" ] }, { "page": "distribution_plot", "title": "State Distribution Plot Over Time", "topics": [ "distribution_plot" ] }, { "page": "estimate_network", "title": "Estimate a Network (Deprecated)", "topics": [ "estimate_network" ] }, { "page": "euler_characteristic", "title": "Euler Characteristic", "topics": [ "euler_characteristic" ] }, { "page": "evaluate_links", "title": "Evaluate Link Predictions Against Known Edges", "topics": [ "evaluate_links" ] }, { "page": "extract_edges", "title": "Extract Edge List with Weights", "topics": [ "extract_edges" ] }, { "page": "extract_initial_probs", "title": "Extract Initial Probabilities from Model", "topics": [ "extract_initial_probs" ] }, { "page": "extract_transition_matrix", "title": "Extract Transition Matrix from Model", "topics": [ "extract_transition_matrix" ] }, { "page": "get_estimator", "title": "Retrieve a Registered Estimator", "topics": [ "get_estimator" ] }, { "page": "group_regulation_long", "title": "Group Regulation in Collaborative Learning (Long Format)", "topics": [ "group_regulation_long" ] }, { "page": "hypergraph_centrality", "title": "Hypergraph eigenvector centralities", "topics": [ "hypergraph_centrality" ] }, { "page": "hypergraph_measures", "title": "Structural measures for a hypergraph", "topics": [ "hypergraph_measures", "print.hypergraph_measures" ] }, { "page": "learning_activities", "title": "Online Learning Activity Indicators", "topics": [ "learning_activities" ] }, { "page": "list_estimators", "title": "List All Registered Estimators", "topics": [ "list_estimators" ] }, { "page": "long_to_wide", "title": "Convert Long Format to Wide Sequences", "topics": [ "long_to_wide" ] }, { "page": "long-data", "title": "Human-AI Vibe Coding Interaction Data (Long Format)", "topics": [ "ai_long", "human_long", "long-data" ] }, { "page": "magnitude_difference", "title": "Magnitude difference between the frequency and probability views", "topics": [ "magnitude_difference", "plot.magnitude_difference", "print.magnitude_difference" ] }, { "page": "mark_first_state", "title": "Mark leading-NA cells with an explicit state label.", "topics": [ "mark_first_state" ] }, { "page": "mark_terminal_state", "title": "Mark terminal-NA cells with an explicit state label.", "topics": [ "mark_terminal_state" ] }, { "page": "markov_order_test", "title": "Test the Markov order of a sequential process", "topics": [ "markov_order_test" ] }, { "page": "markov_stability", "title": "Markov Stability Analysis", "topics": [ "markov_stability", "plot.net_markov_stability" ] }, { "page": "mogen_transitions", "title": "Extract Transition Table from a MOGen Model", "topics": [ "mogen_transitions" ] }, { "page": "mosaic_plot", "title": "Mosaic Plot of a Network's Transition or Co-occurrence Counts", "topics": [ "mosaic_plot", "mosaic_plot.default", "mosaic_plot.htna", "mosaic_plot.matrix", "mosaic_plot.mcml", "mosaic_plot.netobject", "mosaic_plot.netobject_group", "mosaic_plot.table" ] }, { "page": "nct", "title": "Network Comparison Test", "topics": [ "nct" ] }, { "page": "net_aggregate_weights", "title": "Aggregate Edge Weights", "topics": [ "net_aggregate_weights" ] }, { "page": "net_centrality", "title": "Compute Centrality Measures for a Network", "topics": [ "net_centrality" ] }, { "page": "network_reliability", "title": "Split-Half Reliability for Network Estimates", "topics": [ "network_reliability" ] }, { "page": "passage_time", "title": "Mean First Passage Times", "topics": [ "passage_time", "plot.net_mpt", "summary.net_mpt" ] }, { "page": "path_counts", "title": "Count Path Frequencies in Trajectory Data", "topics": [ "path_counts" ] }, { "page": "path_dependence", "title": "Per-Context Path Dependence at Order k", "topics": [ "path_dependence" ] }, { "page": "pathways", "title": "Extract Pathways from Higher-Order Network Objects", "topics": [ "pathways", "pathways.netobject", "pathways.net_association_rules", "pathways.net_hon", "pathways.net_hypa", "pathways.net_link_prediction", "pathways.net_mogen" ] }, { "page": "permutation", "title": "Permutation Test for Network Comparison", "topics": [ "permutation" ] }, { "page": "persistence_landscape", "title": "Persistence Landscape", "topics": [ "persistence_landscape" ] }, { "page": "persistent_homology", "title": "Persistent Homology", "topics": [ "persistent_homology" ] }, { "page": "plot_mosaic", "title": "Draw a Marimekko / Mosaic Plot from a Tidy Data Frame", "topics": [ "plot_mosaic" ] }, { "page": "plot_state_frequencies", "title": "Plot State Frequency Distributions", "topics": [ "plot_state_frequencies", "plot_state_frequencies.default", "plot_state_frequencies.htna", "plot_state_frequencies.mcml", "plot_state_frequencies.netobject", "plot_state_frequencies.netobject_group" ] }, { "page": "plot.boot_glasso", "title": "Plot Method for boot_glasso", "topics": [ "plot.boot_glasso" ] }, { "page": "plot.chain_structure", "title": "Plot method for 'chain_structure'.", "topics": [ "plot.chain_structure" ] }, { "page": "plot.cluster_choice", "title": "Plot Method for cluster_choice", "topics": [ "plot.cluster_choice" ] }, { "page": "plot.mmm_compare", "title": "Plot Method for mmm_compare", "topics": [ "plot.mmm_compare" ] }, { "page": "plot.net_association_rules", "title": "Plot Method for net_association_rules", "topics": [ "plot.net_association_rules" ] }, { "page": "plot.net_cluster_diagnostics", "title": "Plot Method for net_cluster_diagnostics", "topics": [ "plot.net_cluster_diagnostics" ] }, { "page": "plot.net_clustering", "title": "Plot Sequence Clustering Results", "topics": [ "plot.net_clustering" ] }, { "page": "plot.net_comparison", "title": "Plot a network comparison", "topics": [ "plot.net_comparison" ] }, { "page": "plot.net_gimme", "title": "Plot Method for net_gimme", "topics": [ "plot.net_gimme" ] }, { "page": "plot.net_honem", "title": "Plot Method for net_honem", "topics": [ "plot.net_honem" ] }, { "page": "plot.net_markov_order", "title": "Plot Method for net_markov_order", "topics": [ "plot.net_markov_order" ] }, { "page": "plot.net_mmm", "title": "Plot Method for net_mmm", "topics": [ "plot.net_mmm" ] }, { "page": "plot.net_mmm_clustering", "title": "Plot Method for MMM Clustering Attribute", "topics": [ "plot.net_mmm_clustering" ] }, { "page": "plot.net_mogen", "title": "Plot Method for net_mogen", "topics": [ "plot.net_mogen" ] }, { "page": "plot.net_path_dependence", "title": "Plot method for 'net_path_dependence'", "topics": [ "plot.net_path_dependence" ] }, { "page": "plot.net_reliability", "title": "Plot Method for net_reliability", "topics": [ "plot.net_reliability" ] }, { "page": "plot.net_sequence_comparison", "title": "Plot Method for net_sequence_comparison", "topics": [ "plot.net_sequence_comparison" ] }, { "page": "plot.net_stability", "title": "Plot Method for net_stability", "topics": [ "plot.net_stability" ] }, { "page": "plot.net_transition_entropy", "title": "Plot method for 'net_transition_entropy'", "topics": [ "plot.net_transition_entropy" ] }, { "page": "plot.persistence_landscape", "title": "Plot Persistence Landscape", "topics": [ "plot.persistence_landscape" ] }, { "page": "plot.persistent_homology", "title": "Plot Persistent Homology", "topics": [ "plot.persistent_homology" ] }, { "page": "plot.q_analysis", "title": "Plot Q-Analysis", "topics": [ "plot.q_analysis" ] }, { "page": "plot.simplicial_complex", "title": "Plot a Simplicial Complex", "topics": [ "plot.simplicial_complex" ] }, { "page": "predict_links", "title": "Predict Missing or Future Links in a Network", "topics": [ "predict_links" ] }, { "page": "predictability", "title": "Compute Node Predictability", "topics": [ "predictability", "predictability.netobject", "predictability.netobject_group", "predictability.netobject_ml" ] }, { "page": "prepare", "title": "Prepare Event Log Data for Network Estimation", "topics": [ "prepare" ] }, { "page": "prepare_for_tna", "title": "Prepare Data for TNA Analysis", "topics": [ "prepare_for_tna" ] }, { "page": "prepare_onehot", "title": "Import One-Hot Encoded Data into Sequence Format", "topics": [ "prepare_onehot" ] }, { "page": "print.boot_glasso", "title": "Print Method for boot_glasso", "topics": [ "print.boot_glasso" ] }, { "page": "print.chain_structure", "title": "Print method for 'chain_structure'.", "topics": [ "print.chain_structure" ] }, { "page": "print.chain_structure_group", "title": "Print method for 'chain_structure_group'.", "topics": [ "print.chain_structure_group" ] }, { "page": "print.cluster_choice", "title": "Print Method for cluster_choice", "topics": [ "print.cluster_choice" ] }, { "page": "print.mcml", "title": "Print Method for mcml", "topics": [ "print.mcml" ] }, { "page": "print.mcml_layer", "title": "Print Method for an mcml Layer", "topics": [ "print.mcml_layer" ] }, { "page": "print.mmm_compare", "title": "Print Method for mmm_compare", "topics": [ "print.mmm_compare" ] }, { "page": "print.nestimate_data", "title": "Print Method for nestimate_data", "topics": [ "print.nestimate_data" ] }, { "page": "print.net_association_rules", "title": "Print Method for net_association_rules", "topics": [ "print.net_association_rules" ] }, { "page": "print.net_bootstrap", "title": "Print Method for net_bootstrap", "topics": [ "print.net_bootstrap" ] }, { "page": "print.net_bootstrap_group", "title": "Print Method for net_bootstrap_group", "topics": [ "print.net_bootstrap_group" ] }, { "page": "print.net_cluster_diagnostics", "title": "Print Method for net_cluster_diagnostics", "topics": [ "print.net_cluster_diagnostics" ] }, { "page": "print.net_clustering", "title": "Print Method for net_clustering", "topics": [ "print.net_clustering" ] }, { "page": "print.net_gimme", "title": "Print Method for net_gimme", "topics": [ "print.net_gimme" ] }, { "page": "print.net_hon", "title": "Print Method for net_hon", "topics": [ "print.net_hon" ] }, { "page": "print.net_honem", "title": "Print Method for net_honem", "topics": [ "print.net_honem" ] }, { "page": "print.net_hypa", "title": "Print Method for net_hypa", "topics": [ "print.net_hypa" ] }, { "page": "print.net_link_prediction", "title": "Print Method for net_link_prediction", "topics": [ "print.net_link_prediction" ] }, { "page": "print.net_markov_order", "title": "Print Method for net_markov_order", "topics": [ "print.net_markov_order" ] }, { "page": "print.net_markov_order_group", "title": "Print method for 'net_markov_order_group'", "topics": [ "print.net_markov_order_group" ] }, { "page": "print.net_markov_stability_group", "title": "Print method for 'net_markov_stability_group'", "topics": [ "print.net_markov_stability_group" ] }, { "page": "print.net_mlvar", "title": "Print method for net_mlvar", "topics": [ "print.net_mlvar" ] }, { "page": "print.net_mmm", "title": "Print Method for net_mmm", "topics": [ "print.net_mmm" ] }, { "page": "print.net_mmm_clustering", "title": "Print Method for MMM Clustering Attribute", "topics": [ "print.net_mmm_clustering" ] }, { "page": "print.net_mogen", "title": "Print Method for net_mogen", "topics": [ "print.net_mogen" ] }, { "page": "print.net_mpt_group", "title": "Print method for 'net_mpt_group'", "topics": [ "print.net_mpt_group" ] }, { "page": "print.net_nct", "title": "Print Method for net_nct", "topics": [ "print.net_nct" ] }, { "page": "print.net_path_dependence", "title": "Print method for 'net_path_dependence'", "topics": [ "print.net_path_dependence" ] }, { "page": "print.net_permutation", "title": "Print Method for net_permutation", "topics": [ "print.net_permutation" ] }, { "page": "print.net_permutation_group", "title": "Print Method for net_permutation_group", "topics": [ "print.net_permutation_group" ] }, { "page": "print.net_reliability", "title": "Print Method for net_reliability", "topics": [ "print.net_reliability" ] }, { "page": "print.net_sequence_comparison", "title": "Print Method for net_sequence_comparison", "topics": [ "print.net_sequence_comparison" ] }, { "page": "print.net_stability", "title": "Print Method for net_stability", "topics": [ "print.net_stability" ] }, { "page": "print.net_stability_group", "title": "Print Method for net_stability_group", "topics": [ "print.net_stability_group" ] }, { "page": "print.net_transition_entropy", "title": "Print method for 'net_transition_entropy'", "topics": [ "print.net_transition_entropy" ] }, { "page": "print.net_transition_entropy_group", "title": "Print method for 'net_transition_entropy_group'", "topics": [ "print.net_transition_entropy_group" ] }, { "page": "print.netobject", "title": "Print Method for Network Object", "topics": [ "print.netobject" ] }, { "page": "print.netobject_group", "title": "Print Method for Group Network Object", "topics": [ "print.netobject_group" ] }, { "page": "print.netobject_ml", "title": "Print Method for Multilevel Network Object", "topics": [ "print.netobject_ml" ] }, { "page": "print.persistence_landscape", "title": "Print Persistence Landscape", "topics": [ "print.persistence_landscape" ] }, { "page": "print.persistent_homology", "title": "Print persistent homology results", "topics": [ "print.persistent_homology" ] }, { "page": "print.q_analysis", "title": "Print Q-analysis results", "topics": [ "print.q_analysis" ] }, { "page": "print.simplicial_complex", "title": "Print a simplicial complex", "topics": [ "print.simplicial_complex" ] }, { "page": "print.summary_chain_structure", "title": "Print method for 'summary.chain_structure'.", "topics": [ "print.summary_chain_structure" ] }, { "page": "print.summary.net_path_dependence", "title": "Print method for 'summary.net_path_dependence'", "topics": [ "print.summary.net_path_dependence" ] }, { "page": "print.summary.net_transition_entropy", "title": "Print method for 'summary.net_transition_entropy'", "topics": [ "print.summary.net_transition_entropy" ] }, { "page": "print.tidy_covariates", "title": "Print method for tidy covariate output", "topics": [ "print.tidy_covariates" ] }, { "page": "print.wtna_boot_mixed", "title": "Print Method for wtna_boot_mixed", "topics": [ "print.wtna_boot_mixed" ] }, { "page": "print.wtna_mixed", "title": "Print Method for wtna_mixed", "topics": [ "print.wtna_mixed" ] }, { "page": "print.wtna_perm_mixed", "title": "Print Method for wtna_perm_mixed", "topics": [ "print.wtna_perm_mixed" ] }, { "page": "q_analysis", "title": "Q-Analysis", "topics": [ "q_analysis" ] }, { "page": "register_estimator", "title": "Register a Network Estimator", "topics": [ "register_estimator" ] }, { "page": "remove_estimator", "title": "Remove a Registered Estimator", "topics": [ "remove_estimator" ] }, { "page": "rename_models", "title": "Rename the models of a 'netobject_group'", "topics": [ "rename_models", "rename_models.default", "rename_models.netobject_group" ] }, { "page": "sequence_compare", "title": "Compare Subsequence Patterns Between Groups", "topics": [ "sequence_compare" ] }, { "page": "sequence_plot", "title": "Sequence Plot (heatmap, index, or distribution)", "topics": [ "sequence_plot" ] }, { "page": "simplicial_degree", "title": "Simplicial Degree", "topics": [ "simplicial_degree" ] }, { "page": "srl_strategies", "title": "Self-Regulated Learning Strategy Frequencies", "topics": [ "srl_strategies" ] }, { "page": "state_distribution", "title": "Per-Class State Distribution as a Tidy Data Frame", "topics": [ "state_distribution", "state_distribution.default", "state_distribution.htna", "state_distribution.mcml", "state_distribution.netobject", "state_distribution.netobject_group" ] }, { "page": "state_freq", "title": "Print, Plot, and Convert a state_freq Object", "topics": [ "as.data.frame.state_freq", "plot.state_freq", "print.state_freq", "state_freq" ] }, { "page": "state_frequencies", "title": "Compute State Frequencies from Trajectory Data", "topics": [ "state_frequencies" ] }, { "page": "summary.boot_glasso", "title": "Summary Method for boot_glasso", "topics": [ "summary.boot_glasso" ] }, { "page": "summary.chain_structure", "title": "Tidy per-state summary of a 'chain_structure'.", "topics": [ "summary.chain_structure" ] }, { "page": "summary.chain_structure_group", "title": "Cross-group comparison of 'chain_structure_group'.", "topics": [ "summary.chain_structure_group" ] }, { "page": "summary.cluster_choice", "title": "Summary Method for cluster_choice", "topics": [ "summary.cluster_choice" ] }, { "page": "summary.mcml", "title": "Summary Method for mcml", "topics": [ "summary.mcml" ] }, { "page": "summary.mmm_compare", "title": "Summary Method for mmm_compare", "topics": [ "summary.mmm_compare" ] }, { "page": "summary.nest_initial_probs", "title": "Summary Method for Initial Probability Vectors", "topics": [ "summary.nest_initial_probs" ] }, { "page": "summary.nest_transition_counts", "title": "Summary Method for Transition Count Matrices", "topics": [ "summary.nest_transition_counts" ] }, { "page": "summary.nest_transition_matrix", "title": "Summary Method for Transition Matrices", "topics": [ "summary.nest_transition_matrix" ] }, { "page": "summary.net_association_rules", "title": "Summary Method for net_association_rules", "topics": [ "summary.net_association_rules" ] }, { "page": "summary.net_bootstrap", "title": "Summary Method for net_bootstrap", "topics": [ "summary.net_bootstrap" ] }, { "page": "summary.net_bootstrap_group", "title": "Summary Method for net_bootstrap_group", "topics": [ "summary.net_bootstrap_group" ] }, { "page": "summary.net_clustering", "title": "Summary Method for net_clustering", "topics": [ "summary.net_clustering" ] }, { "page": "summary.net_gimme", "title": "Summary Method for net_gimme", "topics": [ "summary.net_gimme" ] }, { "page": "summary.net_hon", "title": "Summary Method for net_hon", "topics": [ "summary.net_hon" ] }, { "page": "summary.net_honem", "title": "Summary Method for net_honem", "topics": [ "summary.net_honem" ] }, { "page": "summary.net_hypa", "title": "Summary Method for net_hypa", "topics": [ "summary.net_hypa" ] }, { "page": "summary.net_link_prediction", "title": "Summary Method for net_link_prediction", "topics": [ "summary.net_link_prediction" ] }, { "page": "summary.net_markov_order", "title": "Summary Method for net_markov_order", "topics": [ "summary.net_markov_order" ] }, { "page": "summary.net_mlvar", "title": "Summary method for net_mlvar", "topics": [ "summary.net_mlvar" ] }, { "page": "summary.net_mmm", "title": "Summary Method for net_mmm", "topics": [ "summary.net_mmm" ] }, { "page": "summary.net_mogen", "title": "Summary Method for net_mogen", "topics": [ "summary.net_mogen" ] }, { "page": "summary.net_nct", "title": "Summary Method for net_nct", "topics": [ "summary.net_nct" ] }, { "page": "summary.net_path_dependence", "title": "Summary method for 'net_path_dependence'", "topics": [ "summary.net_path_dependence" ] }, { "page": "summary.net_permutation", "title": "Summary Method for net_permutation", "topics": [ "summary.net_permutation" ] }, { "page": "summary.net_permutation_group", "title": "Summary Method for net_permutation_group", "topics": [ "summary.net_permutation_group" ] }, { "page": "summary.net_reliability", "title": "Summary Method for net_reliability", "topics": [ "summary.net_reliability" ] }, { "page": "summary.net_sequence_comparison", "title": "Summary Method for net_sequence_comparison", "topics": [ "summary.net_sequence_comparison" ] }, { "page": "summary.net_stability", "title": "Summary Method for net_stability", "topics": [ "summary.net_stability" ] }, { "page": "summary.net_stability_group", "title": "Summary Method for net_stability_group", "topics": [ "summary.net_stability_group" ] }, { "page": "summary.net_transition_entropy", 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