Package index • simulab

All functions

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