Return the catalogue of simulation types reachable through
simulate, one row per type, with the underlying function name
and a one-line description. Print it directly.
Note: this lists the explicit-parameter simulators (those returning a
saqr_sim). The separate simulate_data entry
point provides random, seed-driven generation and returns a bare
data.frame; it is not part of this catalogue.
Value
A base data.frame with one row per simulator and columns:
typeThe
typestring passed tosimulate.functionThe name of the underlying simulator function.
descriptionA short one-line description.
Examples
list_simulators()
#> type function
#> 1 ttest simulate_ttest
#> 2 anova simulate_anova
#> 3 correlation simulate_correlation
#> 4 clusters simulate_clusters
#> 5 prediction simulate_prediction
#> 6 regression simulate_regression
#> 7 lpa simulate_lpa
#> 8 lca simulate_lca
#> 9 fa simulate_fa
#> 10 seq_clusters simulate_seq_clusters
#> 11 longitudinal simulate_longitudinal
#> 12 mlm simulate_mlm
#> 13 growth simulate_growth
#> 14 irt simulate_irt
#> 15 survival simulate_survival
#> 16 hmm simulate_hmm
#> description
#> 1 Two-group comparison with known means/SDs (t-test).
#> 2 Multi-group comparison with known group means (one-way ANOVA).
#> 3 Multivariate normal data from an explicit correlation/covariance matrix.
#> 4 Gaussian mixture with known cluster centres and SDs.
#> 5 Regression data with continuous + categorical predictors and known coefficients.
#> 6 Linear regression data with known coefficients and residual SD.
#> 7 Latent profile analysis: continuous indicators from known profiles.
#> 8 Latent class analysis: binary indicators from known class probabilities.
#> 9 Factor analysis: indicators from known loadings and factor structure.
#> 10 Categorical sequences drawn from known cluster-specific generators.
#> 11 Longitudinal panel data with a known within-person VAR(1) process.
#> 12 Multilevel (mixed-effects) data with known fixed and random effects.
#> 13 Latent growth-curve data with known intercept/slope means and variances.
#> 14 Item response data from a known IRT model (difficulty/discrimination).
#> 15 Survival/time-to-event data with known covariate effects (Cox-style).
#> 16 Hidden Markov sequences from known transition/emission matrices.