apply_batch             Apply a function across simulation results
apply_conditions        Apply conditional generation rules
as.data.frame.simulab_sim
                        Convert a simulation component to a tidy data
                        frame
as.data.frame.simulab_tna
                        Tidy tables of a simulated transition network
as_igraph               Convert tidy network data to an igraph object
as_tna_model            Convert a tidy simulab TNA result to a native
                        tna model
assess_tna_reliability
                        Assess split-half TNA reliability
assign_stepped_wedge    Assign a stepped-wedge treatment schedule
assign_treatment        Assign randomized treatment groups
augment_correlated      Add correlated variables to existing data
augment_density         Add an empirical-density variable to existing
                        data
augment_factorial       Add factorial conditions to existing rows
augment_markov          Add a Markov chain to each input row
augment_study           Add declaratively specified variables to
                        existing data
augment_survival        Add one or more survival processes to data
augment_synthetic       Add jointly resampled source variables to
                        existing data
block_correlation       Create clustered-period correlation structures
bootstrap_tna           Bootstrap a TNA model
calibrate_distribution
                        Convert mean/dispersion parameterizations
calibrate_icc           Calibrate random-effect variance for a target
                        ICC
calibrate_logistic      Calibrate logistic coefficients to prevalence
                        and treatment contrast
calibrate_moments       Solve a distribution's parameters from a target
                        mean and variance
calibrate_survival      Calibrate a Weibull survival curve to target
                        points
categorical_formula     Construct a categorical probability formula
combine_competing_risks
                        Combine competing event times
compare_centralities    Compare node centralities between two networks
compare_networks        Compare two weighted networks
compare_tna_models      Compare multiple TNA estimators on the same
                        sequences
components              List the tidy tables available from a
                        simulation
correlation_structure   Create a tidy correlation structure
cross_validate_tna      Cross-validate TNA estimators
define_condition        Define a conditional data-generation rule
define_conditions       Combine conditional data-generation rules into
                        a specification
define_missingness      Define missingness for one variable
define_missingnesses    Combine missingness definitions into a
                        specification
define_survival         Define a survival process
define_survivals        Combine survival definitions into a
                        specification
define_variable         Define one simulated variable
define_variables        Combine variable definitions into a
                        specification
encode_factors          Encode categorical variables
encode_sequences        One-hot encode long-form sequence states
evaluate_edge_recovery
                        Evaluate edge recovery against a known network
evaluate_tna_estimation
                        Evaluate TNA estimation against generated truth
expand_clusters         Expand cluster-level rows into unit-level rows
expand_periods          Expand observations across periods
factorial_design        Create a full factorial design
fit_tna                 Fit a temporal network analysis model
fit_tna_batch           Fit TNA models to multiple datasets
generate_transition_system
                        Generate a transition system
global_name_regions     List global-name regions
global_names            List the global-name catalogue
inject_missingness      Inject MCAR, MAR, or MNAR missingness
learning_state_categories
                        List learning-state categories
learning_states         List categorized learning states for sequence
                        simulation
linear_formula          Construct a linear simulation formula
list_distributions      List the distributions a specification may use
list_simulators         List public simulation and generation verbs
merge_studies           Merge study data by identifiers
missingness_matrix      Generate a missingness mask
mixture_formula         Construct a mixture simulation formula
network_centrality      Calculate tidy network centralities
observe_treatment       Generate observational treatment or exposure
                        groups
observed_data           Apply a missingness mask to complete data
parameter_grid          Generate parameter combinations
print.simulab_sim       Print a simulation result
print.simulab_tna       Print a simulated transition network
read_definitions        Read variable definitions from a CSV file
repeat_variables        Define repeated variables
run_simulation_scenario
                        Run a built-in simulation scenario
sample_global_names     Sample globally diverse names
sample_learning_states
                        Sample learning states reproducibly
sample_tna              Fit TNA to a sequence sample
scenario_grid           Build a replicated scenario grid
select_variables        Select or remove study variables
simulate_anova          Simulate a one-way group design
simulate_bipartite_network
                        Simulate a bipartite network
simulate_clusters       Simulate Gaussian clusters
simulate_copula         Simulate correlated general-distribution
                        variables with a Gaussian copula
simulate_correlated     Simulate correlated Gaussian variables
simulate_correlation    Simulate a correlation design
simulate_data           Simulate data through the unified catalogue
simulate_density        Simulate from an empirical kernel density
simulate_edge_list      Simulate a tidy network edge list
simulate_event_log      Simulate grouped educational event logs
simulate_factors        Simulate a common-factor model
simulate_group_sequences
                        Simulate grouped actor sequences
simulate_group_tna      Simulate sequences and fit a grouped TNA model
simulate_growth         Simulate longitudinal growth trajectories
simulate_hmm            Simulate a hidden Markov model
simulate_irt            Simulate item-response data
simulate_lca            Simulate a latent class model
simulate_longitudinal   Simulate a multivariate longitudinal VAR
                        process
simulate_lpa            Simulate a latent profile model
simulate_markov         Simulate Markov chains
simulate_ml_lpa         Simulate a two-level latent profile model
simulate_multilevel     Simulate a two-level Gaussian model
simulate_multiplex_network
                        Simulate a multiplex network
simulate_network        Simulate a network from common graph models
simulate_network_batches
                        Simulate repeated networks
simulate_network_matrix
                        Simulate a network matrix
simulate_ordinal        Simulate correlated ordinal variables
simulate_prediction     Simulate a prediction design with continuous
                        and categorical predictors
simulate_proportional_survival
                        Simulate proportional-hazards survival data
                        with calibrated censoring
simulate_regression     Simulate a linear regression design
simulate_scenarios      Run a simulator across a scenario grid
simulate_sequence_batches
                        Simulate repeated sequence datasets
simulate_sequence_clusters
                        Simulate sequences from a mixture of transition
                        systems
simulate_sequences      Simulate basic or perturbed state sequences
simulate_spline         Add a spline-generated variable
simulate_study          Simulate a declaratively specified study
simulate_survival       Simulate baseline data with survival outcomes
simulate_synthetic      Simulate row-bootstrap synthetic data
simulate_temporal_network
                        Simulate a temporal network as edge-activity
                        spells
simulate_tna            Simulate one transition network
simulate_tna_batches    Simulate and fit repeated TNA networks
simulate_tna_network    Simulate a grouped TNA transition network
simulate_ttest          Simulate a two-group design
simulate_until_event    Simulate events and retain records through the
                        nth event
simulation_scenarios    List built-in simulation scenarios
spline_basis            Compute tidy B-spline basis functions
spline_curves           Compute one or more tidy spline curves
summarize_networks      Summarize multiple networks
summarize_simulations   Summarize simulated numeric variables
summarize_transitions   Count observed transitions in sequence data
summary.simulab_sim     Summarize simulated variables
survival_curve          Compute a tidy Weibull survival curve
trim_events             Trim longitudinal records at an event
                        occurrence
update_definition       Update one variable definition
validate_recovery       Compare recovered estimates with known
                        simulation truth
write_simulation        Export a simulation component
