Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Documentation for package ‘simulab’ version 0.4.10

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-- A --

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
assess_tna_reliability	Assess split-half TNA reliability
assign_stepped_wedge	Assign a stepped-wedge treatment schedule
assign_treatment	Assign randomized treatment groups
as_igraph	Convert tidy network data to an igraph object
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

-- B --

block_correlation	Create clustered-period correlation structures
bootstrap_tna	Bootstrap a TNA model

-- C --

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

-- D --

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

-- E --

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

-- F --

factorial_design	Create a full factorial design
fit_tna	Fit a temporal network analysis model
fit_tna_batch	Fit TNA models to multiple datasets

-- G --

generate_transition_system	Generate a transition system
global_names	List the global-name catalogue
global_name_regions	List global-name regions

-- I --

inject_missingness

Inject MCAR, MAR, or MNAR missingness

-- L --

learning_states	List categorized learning states for sequence simulation
learning_state_categories	List learning-state categories
linear_formula	Construct a linear simulation formula
list_distributions	List the distributions a specification may use
list_simulators	List public simulation and generation verbs

-- M --

merge_studies	Merge study data by identifiers
missingness_matrix	Generate a missingness mask
mixture_formula	Construct a mixture simulation formula

-- N --

network_centrality

Calculate tidy network centralities

-- O --

observed_data	Apply a missingness mask to complete data
observe_treatment	Generate observational treatment or exposure groups

-- P --

parameter_grid	Generate parameter combinations
print.simulab_sim	Print a simulation result
print.simulab_tna	Print a simulated transition network

-- R --

read_definitions	Read variable definitions from a CSV file
repeat_variables	Define repeated variables
run_simulation_scenario	Run a built-in simulation scenario

-- S --

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_sequences	Simulate basic or perturbed state sequences
simulate_sequence_batches	Simulate repeated sequence datasets
simulate_sequence_clusters	Simulate sequences from a mixture of transition systems
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

-- T --

trim_events

Trim longitudinal records at an event occurrence

-- U --

update_definition

Update one variable definition

-- V --

validate_recovery

Compare recovered estimates with known simulation truth

-- W --

write_simulation

Export a simulation component