Saqrlab: A Modern Laboratory for Data Simulation

Saqrlab generates synthetic datasets with known ground-truth parameters so you can test and validate statistical methods. It spans classic designs (t-test, ANOVA, regression, factor analysis), modern data-generating processes (IRT, survival, multilevel, growth curves, hidden Markov, missing-data mechanisms), and a full Temporal Network Analysis toolkit.

Details

Two complementary tiers:

• Explicit-parameter simulators (e.g. simulate_ttest(), simulate_irt(), simulate_mlm(), and the simulate() dispatcher) return a saqr_sim object with $data and the true $params, for parameter-recovery testing.

• Random-parameter generation via simulate_data() returns a bare data frame whose structure is invented from the seed, for stress/robustness testing.

Start with list_simulators() for the catalogue, simulate() to generate, and validate_recovery() to score whether a fitted method recovered the truth. Browse the per-category HTML reference in the package's reference/ directory.

See also

Useful links:

• https://mohsaqr.github.io/Saqrlab/

• https://github.com/mohsaqr/Saqrlab

• Report bugs at https://github.com/mohsaqr/Saqrlab/issues

Author

Maintainer: Mohammed Saqr [email protected]