{ "_id": "6acd0bdda14063b82c3aac42", "Package": "Nestimate", "Title": "Dynamic, Probabilistic, and Higher-Order Network Analysis", "Version": "0.9.26", "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,\nhttps://pak.dynasite.org/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": "2026-10-12 13:18:07 UTC", "RemoteUrl": "https://github.com/mohsaqr/Nestimate", "RemoteRef": "HEAD", "RemoteSha": "57941e9406aa307d8880503386db0e51f4b91fd6", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-12 16:01:03 UTC", "User": "root" }, "Author": "Mohammed Saqr [aut, cre, cph],\nSonsoles López-Pernas [aut],\nKamila Misiejuk [aut]", "Maintainer": "Mohammed Saqr ", "_user": "mohsaqr", "_type": "src", "_file": "Nestimate_0.9.26.tar.gz", "_fileid": "https://r2.ropensci.org/3c14b184d8523ac7e0e48b28db14575638b48a5949e5dad5e37a6754dc915c16", "_filesize": 6349033, "_sha256": "3c14b184d8523ac7e0e48b28db14575638b48a5949e5dad5e37a6754dc915c16", "_expires": "2027-01-20T16:33:30.000Z", "_created": "2026-10-12T16:01:03.000Z", "_published": "2026-10-12T16:33:32.792Z", "_buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 114896670105, "time": 239, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11758018199" }, { "job": 114896670096, "time": 226, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11759081366" }, { "job": 114896669965, "time": 172, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11759403128" }, { "job": 114896670068, "time": 232, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11759687020" }, { "job": 114894593065, "time": 308, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11757898663" }, { "job": 114896670195, "time": 136, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11758477469" }, { "job": 114896670194, "time": 162, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11758577307" }, { "job": 114896670005, "time": 128, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11758222469" }, { "job": 114896670165, "time": 178, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11758582389" } ], "_upstream": "https://github.com/mohsaqr/Nestimate", "_commit": { "id": "57941e9406aa307d8880503386db0e51f4b91fd6", "author": "mohsaqr ", "committer": "mohsaqr ", "message": "Nestimate 0.9.26: build_hypa() calibrated null and the paper's test rule\n\nThe HYPA null used Xi = s_out * s_in on observed edges only, never refitted,\nso expected counts did not preserve node strengths and memoryless data\nproduced dozens of anomalies. Xi is now built on every possible De Bruijn\nedge and fitted by continuous iterative proportional fitting; unobserved\npossible paths are scored; at order 2 the expected count equals the\nfirst-order Markov expectation.\n\n- Over-representation uses the strict tail P(X > f) (LaRock et al. 2020).\n- min_count gates over on observed and under on expected counts.\n- p_adjust defaults to \"none\", as in the paper; BH stays optional.\n- Honest convergence reporting (warning nestimate_hypa_no_converge).\n- Input validation: whole scalar min_count, scalar alpha, unique orders;\n internal NA splits trajectories; separator labels rejected.\n- Tests against eight fixtures from the original tlarock/hypa + pathpy.\n", "time": 1791811087 }, "_maintainer": { "name": "Mohammed Saqr", "email": "saqr@saqr.me", "login": "loxavian", "description": "", "uuid": 52792889 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "ggplot2", "role": "Imports" }, { "package": "data.table", "role": "Imports" }, { "package": "cluster", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "brglm2", "role": "Imports" }, { "package": "nnet", "role": "Imports" }, { "package": "idiographic", "version": ">= 0.3.4", "role": "Imports" }, { "package": "psychnets", "version": ">= 0.5.2", "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": "Suggests" }, { "package": "corpcor", "role": "Suggests" }, { "package": "Matrix", "role": "Suggests" }, { "package": "cograph", "version": ">= 2.4.4", "role": "Suggests" }, { "package": "ggfittext", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "tna", "role": "Suggests" } ], "_owner": "mohsaqr", "_selfowned": true, "_usedby": 2, "_updates": [ { "week": "2026-12", "n": 14 }, { "week": "2026-13", "n": 61 }, { "week": "2026-14", "n": 10 }, { "week": "2026-15", "n": 10 }, { "week": "2026-16", "n": 9 }, { "week": "2026-17", "n": 18 }, { "week": "2026-18", "n": 27 }, { "week": "2026-19", "n": 16 }, { "week": "2026-20", "n": 8 }, { "week": "2026-21", "n": 8 }, { "week": "2026-22", "n": 3 }, { "week": "2026-23", "n": 3 }, { "week": "2026-24", "n": 4 }, { "week": "2026-25", "n": 9 }, { "week": "2026-26", "n": 6 }, { "week": "2026-27", "n": 7 }, { "week": "2026-28", "n": 9 }, { "week": "2026-30", "n": 4 }, { "week": "2026-34", "n": 1 }, { "week": "2026-35", "n": 8 }, { "week": "2026-37", "n": 2 }, { "week": "2026-38", "n": 9 }, { "week": "2026-40", "n": 7 }, { "week": "2026-41", "n": 4 }, { "week": "2026-42", "n": 2 } ], "_tags": [], "_stars": 3, "_contributors": [ { "user": "mohsaqr", "count": 280, "uuid": 194697827 }, { "user": "sonsoleslp", "count": 11, "uuid": 10948559 } ], "_userbio": { "uuid": 194697827, "type": "user", "name": "Mohammed Saqr", "followers": 14, "description": "Professor of Computer Science" }, "_downloads": { "count": 385, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/Nestimate" }, "_devurl": "https://github.com/mohsaqr/nestimate", "_pkgdown": "https://pak.dynasite.org/Nestimate/", "_searchresults": 29, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/Nestimate.html", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_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" }, { "version": "0.8.0", "date": "2026-07-10" }, { "version": "0.8.5", "date": "2026-08-21" }, { "version": "0.9.24", "date": "2026-10-07" } ], "_exports": [ "action_to_onehot", "actor_endpoints", "as_htna", "as_netdifference", "as_netobject", "as_networks", "as_tna", "association_rules", "bayes_compare", "betti_numbers", "bipartite_groups", "boot_glasso", "bootstrap_network", "bottleneck_distance", "build_atna", "build_clusters", "build_cna", "build_cor", "build_ftna", "build_gimme", "build_glasso", "build_hon", "build_honem", "build_hypa", "build_hypergraph", "build_ising", "build_mcml", "build_mcml_pc", "build_mlvar", "build_mmm", "build_mogen", "build_network", "build_pcor", "build_simplicial", "build_tna", "casedrop_reliability", "centrality_stability", "certainty", "chain_structure", "clique_expansion", "cluster_choice", "cluster_data", "cluster_diagnostics", "cluster_mmm", "cluster_network", "cluster_summary", "coefs", "compare_mmm", "compare_model", "compare_networks", "composites", "convert_sequence_format", "cooccurrence", "distribution_plot", "edge_differences", "effects_table", "entropy_bayes", "entropy_network", "entropy_trajectory", "estimate_network", "euler_characteristic", "evaluate_links", "extract_edges", "extract_initial_probs", "extract_pathways", "extract_transition_matrix", "frequencies", "get_estimator", "global_differences", "hypergraph_centrality", "hypergraph_cluster", "hypergraph_laplacian", "hypergraph_measures", "hypergraph_transduction", "item_loadings", "list_estimators", "loading_stability", "long_to_wide", "macro_network", "magnitude_difference", "mark_first_state", "mark_terminal_state", "markov_order_test", "markov_stability", "mogen_transitions", "mosaic_analysis", "mosaic_plot", "nct", "net_aggregate_weights", "net_centrality", "net_deprune", "net_edge_betweenness", "net_prune", "net_pruning_details", "net_reprune", "network_metrics", "network_reliability", "node_differences", "outcome_model", "passage_time", "path_counts", "path_dependence", "pathways", "permutation", "permutation_diagnostics", "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", "session_ids", "set_state_colors", "simplicial_degree", "simplicial_features", "state_colors", "state_colors<-", "state_distribution", "state_frequencies", "subtract_networks", "transition_entropy", "validate_netobject", "verify_simplicial", "vertex_bootstrap", "vertex_compare", "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": "chatgpt_srl", "class": [ "data.frame" ], "fields": [ "CSU", "IV", "SE", "SR", "TA" ], "rows": 1000, "table": true, "tojson": true }, { "name": "group_regulation_long", "title": "Group Regulation in Collaborative Learning (Long Format)", "object": "group_regulation_long", "class": [ "data.frame" ], "fields": [ "Actor", "Achiever", "Group", "Course", "Time", "Action" ], "rows": 27533, "table": true, "tojson": true }, { "name": "human_long", "title": "Human-AI Vibe Coding Interaction Data (Long Format)", "object": "human_long", "class": [ "data.frame" ], "fields": [ "message_id", "project", "session_id", "timestamp", "session_date", "code", "cluster", "code_order", "order_in_session" ], "rows": 10796, "table": true, "tojson": true }, { "name": "learning_activities", "title": "Online Learning Activity Indicators", "object": "learning_activities", "class": [ "data.frame" ], "fields": [ "student", "Reading", "Video", "Forum", "Quiz", "Coding", "Review" ], "rows": 6000, "table": true, "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_htna", "title": "Build a grouped node-level network (htna) from data and a clustering", "topics": [ "as_htna", "as_htna.default", "as_htna.mcml", "as_htna.net_mmm" ] }, { "page": "as_netdifference", "title": "Coerce an inferential comparison to a network difference", "topics": [ "as_netdifference", "as_netdifference.default", "as_netdifference.netdifference", "as_netdifference.net_bayes" ] }, { "page": "as_netobject", "title": "Coerce a network object to a Nestimate netobject", "topics": [ "as_netobject", "as_netobject.cograph_network", "as_netobject.default", "as_netobject.netobject", "as_netobject.psychnet" ] }, { "page": "as_networks", "title": "Promote a psychometric MCML result to a network group", "topics": [ "as_networks", "as_networks.default", "as_networks.mcml_pc" ] }, { "page": "as_tna", "title": "Promote the Layers of an mcml to Networks", "topics": [ "as_tna", "as_tna.default", "as_tna.mcml" ] }, { "page": "association_rules", "title": "Discover Association Rules from Sequential or Transaction Data", "topics": [ "association_rules", "plot.net_association_rules", "print.net_association_rules", "summary.net_association_rules" ] }, { "page": "bayes_compare", "title": "Bayesian Dirichlet-Multinomial comparison of two transition networks", "topics": [ "bayes_compare", "plot.net_bayes", "print.net_bayes", "print.net_bayes_group", "summary.net_bayes", "summary.net_bayes_group" ] }, { "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", "plot.boot_glasso", "print.boot_glasso", "summary.boot_glasso" ] }, { "page": "bootstrap_network", "title": "Bootstrap a Network Estimate", "topics": [ "bootstrap_network", "print.net_bootstrap", "print.net_bootstrap_group", "print.wtna_boot_mixed", "summary.net_bootstrap", "summary.net_bootstrap_group", "summary.wtna_boot_mixed" ] }, { "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", "plot.net_clustering", "print.net_clustering", "print.tidy_covariates", "summary.net_clustering" ] }, { "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", "print.net_hon", "summary.net_hon" ] }, { "page": "build_honem", "title": "Build HONEM Embeddings for Higher-Order Networks", "topics": [ "build_honem", "plot.net_honem", "print.net_honem", "summary.net_honem" ] }, { "page": "build_hypa", "title": "Detect Path Anomalies via HYPA", "topics": [ "build_hypa", "print.net_hypa", "summary.net_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", "print.mcml", "print.mcml_layer", "summary.mcml" ] }, { "page": "build_mcml_pc", "title": "Multi-Cluster Multi-Level Aggregation for Psychometric Networks", "topics": [ "build_mcml_pc", "plot.mcml_pc", "print.mcml_pc", "summary.mcml_pc" ] }, { "page": "build_mlvar", "title": "Build a Multilevel Vector Autoregression (mlVAR) network", "topics": [ "build_mlvar", "print.net_mlvar", "summary.net_mlvar" ] }, { "page": "build_mmm", "title": "Fit a Mixed Markov Model", "topics": [ "build_mmm", "plot.net_mmm", "plot.net_mmm_clustering", "print.net_mmm", "print.net_mmm_clustering", "summary.net_mmm" ] }, { "page": "build_mogen", "title": "Build Multi-Order Generative Model (MOGen)", "topics": [ "build_mogen", "plot.net_mogen", "print.net_mogen", "summary.net_mogen" ] }, { "page": "build_network", "title": "Build a Network", "topics": [ "build_network", "print.netobject", "print.netobject_group", "print.netobject_ml", "print.summary.netobject", "print.summary.netobject_group", "summary.netobject", "summary.netobject_group" ] }, { "page": "build_pcor", "title": "Build a Partial Correlation Network", "topics": [ "build_pcor" ] }, { "page": "build_simplicial", "title": "Build a Simplicial Complex", "topics": [ "build_simplicial", "plot.simplicial_complex", "print.simplicial_complex" ] }, { "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", "plot.net_stability", "print.net_stability", "print.net_stability_group", "summary.net_stability", "summary.net_stability_group" ] }, { "page": "certainty", "title": "Analytic certainty of network edges (Bayesian Dirichlet-Multinomial)", "topics": [ "certainty", "print.net_certainty" ] }, { "page": "chain_structure", "title": "Qualitative structure of a discrete-time Markov chain", "topics": [ "chain_structure", "plot.chain_structure", "print.chain_structure", "print.chain_structure_group", "print.summary_chain_structure", "summary.chain_structure", "summary.chain_structure_group" ] }, { "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", "plot.cluster_choice", "print.cluster_choice", "summary.cluster_choice" ] }, { "page": "cluster_diagnostics", "title": "Cluster Diagnostics", "topics": [ "as.data.frame.net_cluster_diagnostics", "cluster_diagnostics", "plot.net_cluster_diagnostics", "print.net_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", "plot.mmm_compare", "print.mmm_compare", "summary.mmm_compare" ] }, { "page": "compare_model", "title": "Compare two networks descriptively", "topics": [ "compare_model", "compare_model.cograph_network", "compare_model.matrix", "compare_model.netobject", "compare_model.netobject_group", "plot.net_comparison", "print.net_comparison" ] }, { "page": "compare_networks", "title": "Compare two or more networks", "topics": [ "compare_networks", "plot.net_network_comparison", "print.net_network_comparison" ] }, { "page": "comparison_tables", "title": "Tables of a network comparison", "topics": [ "comparison_tables", "edge_differences", "global_differences", "network_metrics", "node_differences", "print.net_table", "summary.net_network_comparison" ] }, { "page": "composites", "title": "Cluster Scores From a Psychometric MCML Fit", "topics": [ "composites", "composites.mcml_pc" ] }, { "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": "effects_table", "title": "Effect Table of a Fitted Outcome Model", "topics": [ "effects_table" ] }, { "page": "entropy_bayes", "title": "Bayesian Transition Entropy", "topics": [ "entropy_bayes", "plot.net_entropy_bayes", "print.net_entropy_bayes", "print.net_entropy_bayes_group", "summary.net_entropy_bayes" ] }, { "page": "entropy_network", "title": "Transition Entropy Network", "topics": [ "entropy_network" ] }, { "page": "entropy_trajectory", "title": "Sliding-Window Transition Entropy Trajectory", "topics": [ "entropy_trajectory", "plot.net_entropy_trajectory", "print.net_entropy_trajectory", "summary.net_entropy_trajectory" ] }, { "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", "summary.nest_initial_probs" ] }, { "page": "extract_pathways", "title": "Cut an Event Log into Pathways", "topics": [ "extract_pathways" ] }, { "page": "extract_transition_matrix", "title": "Extract Transition Matrix from Model", "topics": [ "extract_transition_matrix", "summary.nest_transition_matrix" ] }, { "page": "frequencies", "title": "Build a Transition Frequency Matrix", "topics": [ "frequencies", "summary.nest_transition_counts" ] }, { "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_cluster", "title": "Spectral clustering of hypergraph vertices", "topics": [ "as.data.frame.net_hypergraph_cluster", "hypergraph_cluster", "plot.net_hypergraph_cluster", "print.net_hypergraph_cluster", "summary.net_hypergraph_cluster" ] }, { "page": "hypergraph_laplacian", "title": "Normalized hypergraph Laplacian", "topics": [ "hypergraph_laplacian" ] }, { "page": "hypergraph_measures", "title": "Structural measures for a hypergraph", "topics": [ "hypergraph_measures", "print.hypergraph_measures" ] }, { "page": "hypergraph_transduction", "title": "Transductive label spreading on a hypergraph", "topics": [ "as.data.frame.net_hypergraph_transduction", "hypergraph_transduction", "plot.net_hypergraph_transduction", "print.net_hypergraph_transduction", "summary.net_hypergraph_transduction" ] }, { "page": "item_loadings", "title": "Item Diagnostics From a Psychometric MCML Fit", "topics": [ "item_loadings", "item_loadings.mcml_pc" ] }, { "page": "learning_activities", "title": "Online Learning Activity Indicators", "topics": [ "learning_activities" ] }, { "page": "list_estimators", "title": "List All Registered Estimators", "topics": [ "list_estimators" ] }, { "page": "loading_stability", "title": "Composite-Weight Stability Under Case Resampling", "topics": [ "loading_stability", "plot.pc_loading_stability", "print.pc_loading_stability" ] }, { "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": "macro_network", "title": "Cluster-Level Network, With One Cluster Expanded", "topics": [ "macro_network" ] }, { "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", "plot.net_markov_order", "print.net_markov_order", "print.net_markov_order_group", "summary.net_markov_order" ] }, { "page": "markov_stability", "title": "Markov Stability Analysis", "topics": [ "markov_stability", "plot.net_markov_stability", "print.net_markov_stability", "print.net_markov_stability_group", "summary.net_markov_stability" ] }, { "page": "mogen_transitions", "title": "Extract Transition Table from a MOGen Model", "topics": [ "mogen_transitions" ] }, { "page": "mosaic_analysis", "title": "Two-variable mosaic analysis (chi-square test + flat mosaic)", "topics": [ "mosaic_analysis", "plot.mosaic_analysis", "print.mosaic_analysis", "summary.mosaic_analysis" ] }, { "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", "print.net_nct", "summary.net_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", "plot.net_centrality", "plot.net_centrality_group" ] }, { "page": "net_deprune", "title": "Undo Network Pruning", "topics": [ "net_deprune", "net_deprune.default", "net_deprune.netobject", "net_deprune.netobject_group" ] }, { "page": "net_edge_betweenness", "title": "Edge Betweenness Network", "topics": [ "net_edge_betweenness", "net_edge_betweenness.default", "net_edge_betweenness.netobject", "net_edge_betweenness.netobject_group", "plot.net_edge_betweenness" ] }, { "page": "net_prune", "title": "Prune a Network's Edges", "topics": [ "net_prune", "net_prune.default", "net_prune.netobject", "net_prune.netobject_group" ] }, { "page": "net_pruning_details", "title": "Report Network Pruning Details", "topics": [ "net_pruning_details", "net_pruning_details.default", "net_pruning_details.netobject", "net_pruning_details.netobject_group", "print.net_pruning_details" ] }, { "page": "net_reprune", "title": "Re-apply Network Pruning", "topics": [ "net_reprune", "net_reprune.default", "net_reprune.netobject", "net_reprune.netobject_group" ] }, { "page": "network_reliability", "title": "Split-Half Reliability for Network Estimates", "topics": [ "network_reliability", "plot.net_reliability", "print.net_reliability", "summary.net_reliability" ] }, { "page": "outcome_model", "title": "Model Unit-Level Outcomes from Sequence or Network Predictors", "topics": [ "outcome_model", "plot.net_outcome_model", "print.net_outcome_model", "summary.net_outcome_model" ] }, { "page": "passage_time", "title": "Mean First Passage Times", "topics": [ "passage_time", "plot.net_mpt", "print.net_mpt", "print.net_mpt_group", "print.summary.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", "plot.net_path_dependence", "print.net_path_dependence", "print.summary.net_path_dependence", "summary.net_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", "print.net_permutation", "print.net_permutation_group", "print.wtna_perm_mixed", "summary.net_permutation", "summary.net_permutation_group", "summary.wtna_perm_mixed" ] }, { "page": "permutation_diagnostics", "title": "Does Nesting Bias a Permutation Test?", "topics": [ "permutation_diagnostics" ] }, { "page": "persistence_landscape", "title": "Persistence Landscape", "topics": [ "persistence_landscape", "plot.persistence_landscape", "print.persistence_landscape" ] }, { "page": "persistent_homology", "title": "Persistent Homology", "topics": [ "persistent_homology", "plot.persistent_homology", "print.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": [ "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" ] }, { "page": "predict_links", "title": "Predict Missing or Future Links in a Network", "topics": [ "predict_links", "print.net_link_prediction", "summary.net_link_prediction" ] }, { "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", "print.nestimate_data" ] }, { "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": "q_analysis", "title": "Q-Analysis", "topics": [ "plot.q_analysis", "print.q_analysis", "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": [ "plot.net_sequence_comparison", "print.net_sequence_comparison", "sequence_compare", "summary.net_sequence_comparison" ] }, { "page": "sequence_plot", "title": "Sequence Plot (heatmap, index, or distribution)", "topics": [ "print.mcml_sequence_plot", "sequence_plot" ] }, { "page": "session_ids", "title": "The session behind each sequence", "topics": [ "session_ids", "session_ids.default", "session_ids.netobject", "session_ids.net_clustering", "session_ids.net_mmm" ] }, { "page": "set_state_colors", "title": "Set the state colours carried by a network object", "topics": [ "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<-" ] }, { "page": "simplicial_degree", "title": "Simplicial Degree", "topics": [ "simplicial_degree" ] }, { "page": "simplicial_features", "title": "Tidy Topological Features for One or Many Networks", "topics": [ "simplicial_features" ] }, { "page": "srl_strategies", "title": "Self-Regulated Learning Strategy Frequencies", "topics": [ "srl_strategies" ] }, { "page": "state_colors", "title": "The state colours an object will draw with", "topics": [ "state_colors", "state_colors.default", "state_colors.htna", "state_colors.mcml", "state_colors.netobject", "state_colors.netobject_group" ] }, { "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_frequencies", "title": "Compute State Frequencies from Trajectory Data", "topics": [ "state_frequencies" ] }, { "page": "subtract_networks", "title": "Subtract one network from another", "topics": [ "print.netdifference", "subtract_networks" ] }, { "page": "trajectories", "title": "Student Engagement Trajectories", "topics": [ "trajectories" ] }, { "page": "transition_entropy", "title": "Transition Entropy of a Markov Chain", "topics": [ "plot.net_transition_entropy", "print.net_transition_entropy", "print.net_transition_entropy_group", "print.summary.net_transition_entropy", "summary.net_transition_entropy", "transition_entropy" ] }, { "page": "validate_netobject", "title": "Validate a netobject / cograph_network against the shared schema", "topics": [ "validate_netobject" ] }, { "page": "verify_simplicial", "title": "Verify Simplicial Complex Against igraph", "topics": [ "verify_simplicial" ] }, { "page": "vertex_bootstrap", "title": "Vertex Bootstrap for Network-Level Statistics", "topics": [ "plot.net_vertex_bootstrap", "print.net_vertex_bootstrap", "summary.net_vertex_bootstrap", "vertex_bootstrap" ] }, { "page": "vertex_compare", "title": "Compare Network-Level Statistics of Two Networks", "topics": [ "plot.net_vertex_comparison", "print.net_vertex_comparison", "summary.net_vertex_comparison", "vertex_compare" ] }, { "page": "wide_to_long", "title": "Convert Wide Sequences to Long Format", "topics": [ "wide_to_long" ] }, { "page": "wtna", "title": "Window-based Transition Network Analysis", "topics": [ "print.wtna_mixed", "wtna" ] } ], "_pkglogo": "https://github.com/mohsaqr/Nestimate/raw/HEAD/man/figures/logo.svg", "_readme": "https://github.com/mohsaqr/Nestimate/raw/HEAD/README.md", "_rundeps": [ "brglm2", "cli", "cluster", "cpp11", "data.table", "enrichwith", "farver", "ggplot2", "glue", "gtable", "idiographic", "isoband", "labeling", "lattice", "lifecycle", "MASS", "Matrix", "nleqslv", "nnet", "numDeriv", "psychnets", "R6", "RColorBrewer", "rlang", "S7", "scales", "statmod", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "markov-stability.Rmd", "filename": "markov-stability.html", "title": "Markov Stability Analysis", "author": "Mohammed Saqr, Sonsoles López-Pernas, Kamila Misiejuk", "engine": "knitr::rmarkdown", "headings": [ "Data", "Selecting mostly-active learners", "Sequence plots", "Transition networks", "Mean First Passage Times", "Stationary distribution", "Markov Stability" ], "created": "2026-04-18 18:48:35", "modified": "2026-05-20 20:23:58", "commits": 4 }, { "source": "transition-networks.Rmd", "filename": "transition-networks.html", "title": "Network Estimation and Analysis with Nestimate", "author": "Mohammed Saqr, Sonsoles López-Pernas, Kamila Misiejuk", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-03-25 07:15:35", "modified": "2026-05-22 08:05:02", "commits": 13 }, { "source": "clustering.Rmd", "filename": "clustering.html", "title": "Sequence Clustering", "author": "Mohammed Saqr, Sonsoles López-Pernas, Kamila Misiejuk", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-03-27 22:30:12", "modified": "2026-07-21 06:24:55", "commits": 15 }, { "source": "sequence-comparison.Rmd", "filename": "sequence-comparison.html", "title": "Sequence Pattern Comparison: Early vs Late Human-AI Interactions", "author": "Mohammed Saqr, Sonsoles López-Pernas, Kamila Misiejuk", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-04-18 18:48:35", "modified": "2026-05-20 20:23:58", "commits": 5 }, { "source": "sequence-plots.Rmd", "filename": "sequence-plots.html", "title": "Sequence Plots: heatmap, index, and distribution", "author": "Mohammed Saqr, Sonsoles López-Pernas, Kamila Misiejuk", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-04-18 08:52:52", "modified": "2026-05-20 20:23:58", "commits": 5 }, { "source": "transition-entropy.Rmd", "filename": "transition-entropy.html", "title": "Transition Matrix Entropy", "author": "Mohammed Saqr", "engine": "knitr::rmarkdown", "headings": [ "The entropy of a transition network", "Which states are decision points?", "The entropy network", "Entropy over time", "Bayesian estimation", "When to use which", "References" ], "created": "2026-08-21 13:53:48", "modified": "2026-08-21 13:53:48", "commits": 1 } ], "_score": 7.592731766393962, "_indexed": true, "_nocasepkg": "nestimate", "_universes": [ "mohsaqr", "loxavian" ], "_previous": "0.9.25", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.9.26", "date": "2026-10-12T16:03:59.000Z", "distro": "resolute", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/72e37b470407b0adcf6d7dd9d8582f6dd8c030e06490deaf6fc55bb1716b7db5", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.6.1", "os": "linux", "version": "0.9.26", "date": "2026-10-12T16:03:57.000Z", "distro": "resolute", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/3c8f5837d66baddcf09a5d9771fdd2212ed9e5acefaebbdef77e69c7ddacd5b3", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.5.3", "os": "mac", "version": "0.9.26", "date": "2026-10-12T16:26:28.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/bc12d9a4f6d3d2d97b720055be28aca4a09925db6304a0e6370139d03cd8b6c9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.6.1", "os": "mac", "version": "0.9.26", "date": "2026-10-12T16:31:12.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/483165ad10d21856b2367c2b48d306877f8f8fd13097753ff12833887e724541", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.6.0", "os": "wasm", "version": "0.9.26", "date": "2026-10-12T16:04:14.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/d00cb02673dddd4f9c3751fee2dc80666a535aaaee207f9dc30c7e0f44eff78e", "status": "success", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.7.0", "os": "win", "version": "0.9.26", "date": "2026-10-12T16:03:04.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/90e83db15d519d2c6609e391f6367ef5dae73c4406ed1af5cd06bef45fd2c261", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.5.3", "os": "win", "version": "0.9.26", "date": "2026-10-12T16:02:49.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/a5f1f929613e0c7eb03156904586bdc63593e69234c26e9d2dbdd33fc658759c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" }, { "r": "4.6.1", "os": "win", "version": "0.9.26", "date": "2026-10-12T16:03:18.000Z", "commit": "57941e9406aa307d8880503386db0e51f4b91fd6", "fileid": "https://r2.ropensci.org/6dd4563e3f910d179ea96a5b43f848c017d5f49283639371fe1ac8df5b2f094c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/38273862202" } ] }