Man pages for simulab
Simulates a Wide Variety of Data Shapes, Sizes and Distributions
| 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 |
| assess_tna_reliability | Assess split-half TNA reliability |
| assign_stepped_wedge | Assign a stepped-wedge treatment schedule |
| assign_treatment | Assign randomized treatment groups |
| as_tna_model | Convert a tidy simulab TNA result to a native tna model |
| 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... |
| calibrate_moments | Solve a distribution's parameters from a target mean and... |
| 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... |
| 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 |
| observed_data | Apply a missingness mask to complete data |
| observe_treatment | Generate observational treatment or exposure groups |
| 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 |
| simulab-package | simulab: Unified simulation of statistical and study data |
| 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... |
| 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... |
| simulate_proportional_survival | Simulate proportional-hazards survival data with calibrated... |
| 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 |
