{
  "_id": "6ac294b8d743750d376d8f47",
  "Package": "simulab",
  "Title": "Simulates a Wide Variety of Data Shapes, Sizes and Distributions",
  "Version": "0.4.10",
  "Authors@R": "c(\nperson(\"Mohammed\", \"Saqr\", email = \"saqr@saqr.me\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-5881-3109\")),\nperson(\"Sonsoles\", \"L\\u00f3pez-Pernas\", email = \"sonsoles.lopez@uef.fi\",\nrole = c(\"aut\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-9621-1392\"))\n)",
  "Description": "Generates a wide variety of data, in different shapes,\nsample sizes, distributions and parameters. The parameters are\neither drawn at random or set in advance, and in both cases\nthey are stored with the data, so they are known and their\nrecovery can be tested. Generators cover declared variables\nwith treatment assignment and missing values, correlated and\nclustered data, latent profile, latent class, factor and item\nresponse models, longitudinal and multilevel processes,\nsurvival times with competing risks, sequences of states, and\nstatic, bipartite, multiplex, temporal and transition networks.\nEvery generator returns batches of reproducible data sets for\nthe evaluation of statistical methods, the testing of models\nand software, and teaching; simulated transition networks are\nnative 'tna' models.",
  "URL": "https://pak.dynasite.org/simulab/,\nhttps://github.com/mohsaqr/simulab",
  "BugReports": "https://github.com/mohsaqr/simulab/issues",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr, rmarkdown",
  "Repository": "https://mohsaqr.r-universe.dev",
  "Date/Publication": "2026-10-04 14:43:52 UTC",
  "RemoteUrl": "https://github.com/mohsaqr/simulab",
  "RemoteRef": "HEAD",
  "RemoteSha": "df0e97692099233eb5ca85947794261e9e57f268",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-04 17:49:18 UTC",
    "User": "root"
  },
  "Author": "Mohammed Saqr [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-5881-3109>),\nSonsoles López-Pernas [aut, cph] (ORCID:\n<https://orcid.org/0000-0002-9621-1392>)",
  "Maintainer": "Mohammed Saqr <saqr@saqr.me>",
  "_user": "mohsaqr",
  "_type": "src",
  "_file": "simulab_0.4.10.tar.gz",
  "_fileid": "https://r2.ropensci.org/b94c934f5a94fae4436144000b62c58b4f4eeb6a608a8a13ead0d7fdbe67b121",
  "_filesize": 800583,
  "_sha256": "b94c934f5a94fae4436144000b62c58b4f4eeb6a608a8a13ead0d7fdbe67b121",
  "_expires": "2027-01-12T18:02:30.000Z",
  "_created": "2026-10-04T17:49:18.000Z",
  "_published": "2026-10-04T18:02:31.886Z",
  "_jobs": [
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      "job": 111494041183,
      "time": 175,
      "config": "linux-devel-x86_64",
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      "check": "OK",
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  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/mohsaqr/actions/runs/37221709443",
  "_status": "success",
  "_upstream": "https://github.com/mohsaqr/simulab",
  "_commit": {
    "id": "df0e97692099233eb5ca85947794261e9e57f268",
    "author": "mohsaqr <hamada@saqr.me>",
    "committer": "mohsaqr <hamada@saqr.me>",
    "message": "simulab 0.4.10: skip large-sample calibration tests on CRAN\n\nFour calibration and sweep tests (Markov and TNA recovery, Markov initial\nprobabilities, the seeded batch sweep) take most of the test time. They run\non CI with NOT_CRAN=true and are skipped on CRAN. Test time under CRAN\nsettings falls from 24.6 s to 9.1 s locally.\n",
    "time": 1791125032
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  "_maintainer": {
    "name": "Mohammed Saqr",
    "email": "saqr@saqr.me",
    "login": "loxavian",
    "description": "",
    "uuid": 52792889,
    "orcid": "0000-0001-5881-3109"
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
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      "package": "R",
      "version": ">= 4.1.0",
      "role": "Depends"
    },
    {
      "package": "stats",
      "role": "Imports"
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    {
      "package": "utils",
      "role": "Imports"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
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    {
      "package": "knitr",
      "role": "Suggests"
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      "package": "igraph",
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    {
      "package": "rmarkdown",
      "role": "Suggests"
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    {
      "package": "simstudy",
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    },
    {
      "package": "survival",
      "role": "Suggests"
    },
    {
      "package": "tna",
      "version": ">= 1.2.3",
      "role": "Suggests"
    }
  ],
  "_owner": "mohsaqr",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-35",
      "n": 9
    },
    {
      "week": "2026-38",
      "n": 1
    },
    {
      "week": "2026-39",
      "n": 3
    },
    {
      "week": "2026-40",
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  ],
  "_tags": [],
  "_stars": 0,
  "_contributors": [
    {
      "user": "mohsaqr",
      "count": 12,
      "uuid": 194697827
    },
    {
      "user": "loxavian",
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      "uuid": 52792889
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  ],
  "_userbio": {
    "uuid": 194697827,
    "type": "user",
    "name": "Mohammed Saqr",
    "followers": 13,
    "description": "Professor of Computer Science"
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/simulab"
  },
  "_devurl": "https://github.com/mohsaqr/simulab",
  "_pkgdown": "https://pak.dynasite.org/simulab/",
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  "_rbuild": "4.6.1",
  "_assets": [
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    "extra/citation.json",
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    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/simulab.html",
    "LICENSE"
  ],
  "_cranurl": false,
  "_exports": [
    "apply_batch",
    "apply_conditions",
    "as_igraph",
    "as_tna_model",
    "assess_tna_reliability",
    "assign_stepped_wedge",
    "assign_treatment",
    "augment_correlated",
    "augment_density",
    "augment_factorial",
    "augment_markov",
    "augment_study",
    "augment_survival",
    "augment_synthetic",
    "block_correlation",
    "bootstrap_tna",
    "calibrate_distribution",
    "calibrate_icc",
    "calibrate_logistic",
    "calibrate_moments",
    "calibrate_survival",
    "categorical_formula",
    "combine_competing_risks",
    "compare_centralities",
    "compare_networks",
    "compare_tna_models",
    "components",
    "correlation_structure",
    "cross_validate_tna",
    "define_condition",
    "define_conditions",
    "define_missingness",
    "define_missingnesses",
    "define_survival",
    "define_survivals",
    "define_variable",
    "define_variables",
    "encode_factors",
    "encode_sequences",
    "evaluate_edge_recovery",
    "evaluate_tna_estimation",
    "expand_clusters",
    "expand_periods",
    "factorial_design",
    "fit_tna",
    "fit_tna_batch",
    "generate_transition_system",
    "global_name_regions",
    "global_names",
    "inject_missingness",
    "learning_state_categories",
    "learning_states",
    "linear_formula",
    "list_distributions",
    "list_simulators",
    "merge_studies",
    "missingness_matrix",
    "mixture_formula",
    "network_centrality",
    "observe_treatment",
    "observed_data",
    "parameter_grid",
    "read_definitions",
    "repeat_variables",
    "run_simulation_scenario",
    "sample_global_names",
    "sample_learning_states",
    "sample_tna",
    "scenario_grid",
    "select_variables",
    "simulate_anova",
    "simulate_bipartite_network",
    "simulate_clusters",
    "simulate_copula",
    "simulate_correlated",
    "simulate_correlation",
    "simulate_data",
    "simulate_density",
    "simulate_edge_list",
    "simulate_event_log",
    "simulate_factors",
    "simulate_group_sequences",
    "simulate_group_tna",
    "simulate_growth",
    "simulate_hmm",
    "simulate_irt",
    "simulate_lca",
    "simulate_longitudinal",
    "simulate_lpa",
    "simulate_markov",
    "simulate_ml_lpa",
    "simulate_multilevel",
    "simulate_multiplex_network",
    "simulate_network",
    "simulate_network_batches",
    "simulate_network_matrix",
    "simulate_ordinal",
    "simulate_prediction",
    "simulate_proportional_survival",
    "simulate_regression",
    "simulate_scenarios",
    "simulate_sequence_batches",
    "simulate_sequence_clusters",
    "simulate_sequences",
    "simulate_spline",
    "simulate_study",
    "simulate_survival",
    "simulate_synthetic",
    "simulate_temporal_network",
    "simulate_tna",
    "simulate_tna_batches",
    "simulate_tna_network",
    "simulate_ttest",
    "simulate_until_event",
    "simulation_scenarios",
    "spline_basis",
    "spline_curves",
    "summarize_networks",
    "summarize_simulations",
    "summarize_transitions",
    "survival_curve",
    "trim_events",
    "update_definition",
    "validate_recovery",
    "write_simulation"
  ],
  "_help": [
    {
      "page": "apply_batch",
      "title": "Apply a function across simulation results",
      "topics": [
        "apply_batch"
      ]
    },
    {
      "page": "apply_conditions",
      "title": "Apply conditional generation rules",
      "topics": [
        "apply_conditions"
      ]
    },
    {
      "page": "as_igraph",
      "title": "Convert tidy network data to an igraph object",
      "topics": [
        "as_igraph"
      ]
    },
    {
      "page": "as_tna_model",
      "title": "Convert a tidy simulab TNA result to a native tna model",
      "topics": [
        "as_tna_model"
      ]
    },
    {
      "page": "as.data.frame.simulab_sim",
      "title": "Convert a simulation component to a tidy data frame",
      "topics": [
        "as.data.frame.simulab_sim"
      ]
    },
    {
      "page": "as.data.frame.simulab_tna",
      "title": "Tidy tables of a simulated transition network",
      "topics": [
        "as.data.frame.simulab_tna"
      ]
    },
    {
      "page": "assess_tna_reliability",
      "title": "Assess split-half TNA reliability",
      "topics": [
        "assess_tna_reliability"
      ]
    },
    {
      "page": "assign_stepped_wedge",
      "title": "Assign a stepped-wedge treatment schedule",
      "topics": [
        "assign_stepped_wedge"
      ]
    },
    {
      "page": "assign_treatment",
      "title": "Assign randomized treatment groups",
      "topics": [
        "assign_treatment"
      ]
    },
    {
      "page": "augment_correlated",
      "title": "Add correlated variables to existing data",
      "topics": [
        "augment_correlated"
      ]
    },
    {
      "page": "augment_density",
      "title": "Add an empirical-density variable to existing data",
      "topics": [
        "augment_density"
      ]
    },
    {
      "page": "augment_factorial",
      "title": "Add factorial conditions to existing rows",
      "topics": [
        "augment_factorial"
      ]
    },
    {
      "page": "augment_markov",
      "title": "Add a Markov chain to each input row",
      "topics": [
        "augment_markov"
      ]
    },
    {
      "page": "augment_study",
      "title": "Add declaratively specified variables to existing data",
      "topics": [
        "augment_study"
      ]
    },
    {
      "page": "augment_survival",
      "title": "Add one or more survival processes to data",
      "topics": [
        "augment_survival"
      ]
    },
    {
      "page": "augment_synthetic",
      "title": "Add jointly resampled source variables to existing data",
      "topics": [
        "augment_synthetic"
      ]
    },
    {
      "page": "block_correlation",
      "title": "Create clustered-period correlation structures",
      "topics": [
        "block_correlation"
      ]
    },
    {
      "page": "bootstrap_tna",
      "title": "Bootstrap a TNA model",
      "topics": [
        "bootstrap_tna"
      ]
    },
    {
      "page": "calibrate_distribution",
      "title": "Convert mean/dispersion parameterizations",
      "topics": [
        "calibrate_distribution"
      ]
    },
    {
      "page": "calibrate_icc",
      "title": "Calibrate random-effect variance for a target ICC",
      "topics": [
        "calibrate_icc"
      ]
    },
    {
      "page": "calibrate_logistic",
      "title": "Calibrate logistic coefficients to prevalence and treatment contrast",
      "topics": [
        "calibrate_logistic"
      ]
    },
    {
      "page": "calibrate_moments",
      "title": "Solve a distribution's parameters from a target mean and variance",
      "topics": [
        "calibrate_moments"
      ]
    },
    {
      "page": "calibrate_survival",
      "title": "Calibrate a Weibull survival curve to target points",
      "topics": [
        "calibrate_survival"
      ]
    },
    {
      "page": "categorical_formula",
      "title": "Construct a categorical probability formula",
      "topics": [
        "categorical_formula"
      ]
    },
    {
      "page": "combine_competing_risks",
      "title": "Combine competing event times",
      "topics": [
        "combine_competing_risks"
      ]
    },
    {
      "page": "compare_centralities",
      "title": "Compare node centralities between two networks",
      "topics": [
        "compare_centralities"
      ]
    },
    {
      "page": "compare_networks",
      "title": "Compare two weighted networks",
      "topics": [
        "compare_networks"
      ]
    },
    {
      "page": "compare_tna_models",
      "title": "Compare multiple TNA estimators on the same sequences",
      "topics": [
        "compare_tna_models"
      ]
    },
    {
      "page": "components",
      "title": "List the tidy tables available from a simulation",
      "topics": [
        "components"
      ]
    },
    {
      "page": "correlation_structure",
      "title": "Create a tidy correlation structure",
      "topics": [
        "correlation_structure"
      ]
    },
    {
      "page": "cross_validate_tna",
      "title": "Cross-validate TNA estimators",
      "topics": [
        "cross_validate_tna"
      ]
    },
    {
      "page": "define_condition",
      "title": "Define a conditional data-generation rule",
      "topics": [
        "define_condition"
      ]
    },
    {
      "page": "define_conditions",
      "title": "Combine conditional data-generation rules into a specification",
      "topics": [
        "define_conditions"
      ]
    },
    {
      "page": "define_missingness",
      "title": "Define missingness for one variable",
      "topics": [
        "define_missingness"
      ]
    },
    {
      "page": "define_missingnesses",
      "title": "Combine missingness definitions into a specification",
      "topics": [
        "define_missingnesses"
      ]
    },
    {
      "page": "define_survival",
      "title": "Define a survival process",
      "topics": [
        "define_survival"
      ]
    },
    {
      "page": "define_survivals",
      "title": "Combine survival definitions into a specification",
      "topics": [
        "define_survivals"
      ]
    },
    {
      "page": "define_variable",
      "title": "Define one simulated variable",
      "topics": [
        "define_variable"
      ]
    },
    {
      "page": "define_variables",
      "title": "Combine variable definitions into a specification",
      "topics": [
        "define_variables"
      ]
    },
    {
      "page": "encode_factors",
      "title": "Encode categorical variables",
      "topics": [
        "encode_factors"
      ]
    },
    {
      "page": "encode_sequences",
      "title": "One-hot encode long-form sequence states",
      "topics": [
        "encode_sequences"
      ]
    },
    {
      "page": "evaluate_edge_recovery",
      "title": "Evaluate edge recovery against a known network",
      "topics": [
        "evaluate_edge_recovery"
      ]
    },
    {
      "page": "evaluate_tna_estimation",
      "title": "Evaluate TNA estimation against generated truth",
      "topics": [
        "evaluate_tna_estimation"
      ]
    },
    {
      "page": "expand_clusters",
      "title": "Expand cluster-level rows into unit-level rows",
      "topics": [
        "expand_clusters"
      ]
    },
    {
      "page": "expand_periods",
      "title": "Expand observations across periods",
      "topics": [
        "expand_periods"
      ]
    },
    {
      "page": "factorial_design",
      "title": "Create a full factorial design",
      "topics": [
        "factorial_design"
      ]
    },
    {
      "page": "fit_tna",
      "title": "Fit a temporal network analysis model",
      "topics": [
        "fit_tna"
      ]
    },
    {
      "page": "fit_tna_batch",
      "title": "Fit TNA models to multiple datasets",
      "topics": [
        "fit_tna_batch"
      ]
    },
    {
      "page": "generate_transition_system",
      "title": "Generate a transition system",
      "topics": [
        "generate_transition_system"
      ]
    },
    {
      "page": "global_name_regions",
      "title": "List global-name regions",
      "topics": [
        "global_name_regions"
      ]
    },
    {
      "page": "global_names",
      "title": "List the global-name catalogue",
      "topics": [
        "global_names"
      ]
    },
    {
      "page": "inject_missingness",
      "title": "Inject MCAR, MAR, or MNAR missingness",
      "topics": [
        "inject_missingness"
      ]
    },
    {
      "page": "learning_state_categories",
      "title": "List learning-state categories",
      "topics": [
        "learning_state_categories"
      ]
    },
    {
      "page": "learning_states",
      "title": "List categorized learning states for sequence simulation",
      "topics": [
        "learning_states"
      ]
    },
    {
      "page": "linear_formula",
      "title": "Construct a linear simulation formula",
      "topics": [
        "linear_formula"
      ]
    },
    {
      "page": "list_distributions",
      "title": "List the distributions a specification may use",
      "topics": [
        "list_distributions"
      ]
    },
    {
      "page": "list_simulators",
      "title": "List public simulation and generation verbs",
      "topics": [
        "list_simulators"
      ]
    },
    {
      "page": "merge_studies",
      "title": "Merge study data by identifiers",
      "topics": [
        "merge_studies"
      ]
    },
    {
      "page": "missingness_matrix",
      "title": "Generate a missingness mask",
      "topics": [
        "missingness_matrix"
      ]
    },
    {
      "page": "mixture_formula",
      "title": "Construct a mixture simulation formula",
      "topics": [
        "mixture_formula"
      ]
    },
    {
      "page": "network_centrality",
      "title": "Calculate tidy network centralities",
      "topics": [
        "network_centrality"
      ]
    },
    {
      "page": "observe_treatment",
      "title": "Generate observational treatment or exposure groups",
      "topics": [
        "observe_treatment"
      ]
    },
    {
      "page": "observed_data",
      "title": "Apply a missingness mask to complete data",
      "topics": [
        "observed_data"
      ]
    },
    {
      "page": "parameter_grid",
      "title": "Generate parameter combinations",
      "topics": [
        "parameter_grid"
      ]
    },
    {
      "page": "print.simulab_sim",
      "title": "Print a simulation result",
      "topics": [
        "print.simulab_sim"
      ]
    },
    {
      "page": "print.simulab_tna",
      "title": "Print a simulated transition network",
      "topics": [
        "print.simulab_tna"
      ]
    },
    {
      "page": "read_definitions",
      "title": "Read variable definitions from a CSV file",
      "topics": [
        "read_definitions"
      ]
    },
    {
      "page": "repeat_variables",
      "title": "Define repeated variables",
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      "title": "Fit TNA to a sequence sample",
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      "title": "Simulate item-response data",
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      "title": "Simulate a latent class model",
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      "source": "getting-started.Rmd",
      "filename": "getting-started.html",
      "title": "Simulating a Wide Variety of Data Shapes, Sizes and Distributions",
      "author": "Mohammed Saqr, Sonsoles López-Pernas",
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      "modified": "2026-10-04 14:05:47",
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