Package: simulab 0.4.10

Mohammed Saqr

simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Generates a wide variety of data, in different shapes, sample sizes, distributions and parameters. The parameters are either drawn at random or set in advance, and in both cases they are stored with the data, so they are known and their recovery can be tested. Generators cover declared variables with treatment assignment and missing values, correlated and clustered data, latent profile, latent class, factor and item response models, longitudinal and multilevel processes, survival times with competing risks, sequences of states, and static, bipartite, multiplex, temporal and transition networks. Every generator returns batches of reproducible data sets for the evaluation of statistical methods, the testing of models and software, and teaching; simulated transition networks are native 'tna' models.

Authors:Mohammed Saqr [aut, cre, cph], Sonsoles López-Pernas [aut, cph]

simulab_0.4.10.tar.gz
simulab_0.4.10.zip(r-4.7-any)simulab_0.4.10.zip(r-4.6-any)simulab_0.4.10.zip(r-4.5-any)
simulab_0.4.10.tgz(r-4.6-any)simulab_0.4.10.tgz(r-4.5-any)
simulab_0.4.10.tar.gz(r-4.7-any)simulab_0.4.10.tar.gz(r-4.6-any)
simulab_0.4.10.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
simulab/json (API)

# Install 'simulab' in R:

install.packages('simulab', repos = c('https://mohsaqr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mohsaqr/simulab/issues

Pkgdown/docs site:https://pak.dynasite.org

On CRAN:

3.30 score 125 exports 0 dependencies

Last updated from:df0e976920. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	175
source / vignettes	OK	255
linux-release	OK	169
macos-release	OK	289
macos-oldrel	OK	213
windows-devel	OK	129
windows-release	OK	138
windows-oldrel	OK	141
wasm-release	OK	123

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

Dependencies:

Simulating a Wide Variety of Data Shapes, Sizes and Distributions

Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-10-04
Started: 2026-08-29

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

Package: simulab 0.4.10

simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)