Package index • Saqrlab

Getting started

One front door for every simulator, plus recovery scoring.

simulate(): Simulate Data via a Unified Dispatcher
list_simulators(): List Available Simulators
saqr_sim(): Create a saqr_sim Object
validate_recovery() print(<recovery_result>) summary(<recovery_result>): Score Parameter Recovery Against Simulated Ground Truth
tidy_simulation_results(): Flatten Simulation Results into One Tidy Data Frame
export_simulation(): Export Simulation Results to CSV

Scenario presets

Named, reproducible simulation recipes.

list_scenarios(): List Available Scenario Presets
get_scenario(): Get a Scenario Recipe
run_scenario(): Run a Whole Scenario

Statistical simulators

Classic designs with explicit ground-truth effect sizes.

simulate_ttest(): Simulate Two-Group Comparison Data (t-test)
simulate_anova(): Simulate Multi-Group Comparison Data (ANOVA)
simulate_correlation(): Simulate Correlated Multivariate Data
simulate_clusters(): Simulate Cluster Data with Known Centers
simulate_prediction(): Simulate Prediction/Regression Data with Known Coefficients
simulate_regression(): Simulate Linear Regression Data with Known Coefficients

Latent-variable simulators

Mixtures, classes, factors, and sequence clusters.

simulate_lpa(): Simulate Latent Profile Analysis Data
simulate_lca(): Simulate Latent Class Analysis Data
simulate_fa(): Simulate Factor Analysis Data with Known Parameters
simulate_seq_clusters(): Simulate Sequence Data with Known Cluster Structure

Longitudinal, multilevel & growth

VAR(1) panels, nested data, and growth curves.

simulate_longitudinal(): Simulate Longitudinal Panel Data
simulate_mlm(): Simulate Two-Level Multilevel (Hierarchical) Data
simulate_growth(): Simulate Latent Growth-Curve Data

IRT, survival & hidden Markov

Item response, time-to-event, and latent-state processes.

simulate_irt(): Simulate Item Response Theory (IRT) Data
simulate_survival(): Simulate Survival Data (Cox Proportional Hazards)
simulate_hmm(): Simulate Hidden Markov Model Sequences

Missing-data mechanisms

Inject MCAR / MAR / MNAR missingness with a known mechanism.

inject_missingness(): Inject Missing Values Under a Known Mechanism (MCAR / MAR / MNAR)

Random-parameter (stress) simulation

Seed-driven structure for robustness testing.

simulate_data(): Simulate Ready-to-Use Statistical Datasets

TNA network simulation

Fitted TNA models, group networks, HTNA/MLNA/MTNA structures.

simulate_tna_network(): Simulate a Single TNA Network
simulate_tna_networks() generate_tna_networks(): Simulate TNA Network Objects
simulate_group_tna_networks() generate_group_tna_networks(): Simulate Group TNA Network Objects
simulate_htna() simulate_mlna() simulate_mtna(): Simulate HTNA/MLNA/MTNA Matrix with Node Types
simulate_tna_datasets() generate_tna_datasets() generate_sequence_data(): Simulate TNA Datasets (Sequences + Models + Probabilities)
simulate_tna_matrix() generate_tna_matrix(): Simulate TNA Transition Matrix with Node Groupings

Sequences

Markov-chain sequences and sampling from fitted models.

simulate_sequences(): Simulate Markov Chain Sequences (Basic)
simulate_sequences_advanced(): Simulate Markov Chain Sequences (Advanced)
simulate_tna_datasets() generate_tna_datasets() generate_sequence_data(): Simulate TNA Datasets (Sequences + Models + Probabilities)
sample_tna(): Sample and Re-estimate TNA Model

Matrices & probabilities

Raw transition matrices and stochastic probability vectors.

simulate_matrix(): Simulate Network Matrix
generate_probabilities(): Generate Transition and Initial Probabilities

Networks & graphs

Social-network structures and format helpers.

simulate_network(): Simulate statnet Network Object
simulate_igraph(): Simulate igraph Network Object
simulate_edge_list(): Simulate Social Network Edge List
simulate_long_data(): Simulate Long Format Sequence Data
simulate_onehot_data(): Simulate One-Hot Encoded Sequence Data

Model fitting

Fit TNA-family models to sequences.

fit_network_model(): Fit a Temporal Network Analysis Model
cross_validate_tna(): Cross-Validate TNA Model Types

Comparison & evaluation

Compare fitted networks, centralities, and estimators.

compare_networks(): Compare Two TNA Networks
compare_centralities(): Compare Centrality Profiles
compare_edge_recovery() calculate_edge_recovery(): Calculate Edge Recovery Metrics
compare_estimation(): Compare Model Estimation Across Simulations
compare_network_estimation() compare_tna_models(): Compare Network Estimation Across TNA Model Types
compare_reliability(): Compare Reliability Across Data Conditions
print(<network_estimation>): Print Network Estimation Results
plot(<network_estimation>): Plot Network Estimation Comparison Results
print(<tna_reliability_comparison>): Print Method for TNA Reliability Comparison
plot(<tna_reliability_comparison>): Plot Method for TNA Reliability Comparison

Simulation studies & bootstrap

Grid experiments, bootstrap stability, and sampling analysis.

run_bootstrap_simulation(): Run Bootstrap Simulation Across Multiple Runs
run_bootstrap_iteration() evaluate_bootstrap(): Run Bootstrap Iteration for a Single Simulation Run
run_grid_simulation(): Run Grid Search Over Simulation Parameters
run_network_simulation(): Run Network Analysis Simulations
run_sampling_analysis(): Run Sampling Analysis on TNA Models
summarize_grid_results() analyze_grid_results(): Summarize Grid Simulation Results
generate_param_grid() create_param_grid(): Generate Parameter Grid for Simulations
batch_fit_models(): Fit Models to Multiple Datasets
batch_apply(): Apply Function to Multiple Models or Datasets

Visualization

Plots for comparisons and sampling distributions.

plot_tna_comparison(): Plot TNA Model Comparison Results
plot_network_estimation(): Plot Network Estimation Results
plot_sampling_distribution(): Plot Sampling Distribution for a Single Metric

Summaries & validation

Summarize results and validate simulation parameters.

summarize_simulation(): Summarize Simulation Results
summarize_networks(): Summarize Multiple Networks
validate_sim_params(): Validate and Standardize Simulation Parameters

Learning states & names

Reference datasets of learning actions and diverse actor names.

LEARNING_STATES: Learning State Verbs for TNA Simulation
GROUP_REGULATION_ACTIONS: Group Regulation Actions
get_learning_states(): Get Learning State Verbs
list_learning_categories(): Get Learning State Categories Summary
select_states() smart_select_states(): Select States for Networks
GLOBAL_NAMES: Global Names Dataset
get_global_names(): Get Global Names
list_name_regions(): List Available Name Regions