idiographic uses a small registry to give estimators and workflows one
stable dispatch interface. Package methods are registered lazily, so the
registry does not depend on source-file load order. Third-party methods can
register either a function or the name of a function available in the
package namespace or calling environment.
Examples
list_estimators()
#> name kind function_name
#> 10 gimme estimator fit_gimme
#> 4 graphical_var estimator fit_graphical_var
#> 5 graphical_var_each estimator fit_graphical_var_each
#> 13 ml estimator fit_ml
#> 6 mlvar estimator fit_mlvar
#> 7 mlvar_bayes estimator fit_mlvar_bayes
#> 8 mlvar_mplus estimator fit_mlvar_mplus
#> 12 rolling_graphical_var estimator fit_rolling_graphical_var
#> 11 rolling_var estimator fit_rolling_var
#> 9 usem estimator fit_usem
#> 1 var estimator fit_var
#> 3 var_bayes estimator fit_var_bayes
#> 2 var_each estimator fit_var_each
#> 16 compare workflow compare_idiographic
#> 17 forecast workflow validate_forecast
#> 14 preprocess workflow preprocess
#> 15 stability workflow estimate_stability
#> aliases
#> 10 fit_gimme, gimme_sem
#> 4 fit_graphical_var, gvar, graphicalvar
#> 5 fit_graphical_var_each, gvar_each
#> 13 fit_ml, idiographic_ml, fit_idiographic_ml, individualized_ml, fit_individualized_ml
#> 6 fit_mlvar, multilevel_var
#> 7 fit_mlvar_bayes, bayes_mlvar, bayesian_mlvar, dsem, native_dsem
#> 8 fit_mlvar_mplus, mplus, mplus_dsem
#> 12 fit_rolling_graphical_var, rolling_gvar
#> 11 fit_rolling_var
#> 9 fit_usem, u_sem
#> 1 fit_var, ols, ols_var
#> 3 fit_var_bayes, bayes_var, bayesian_var
#> 2 fit_var_each, ols_var_each
#> 16 compare_idiographic, model_comparison
#> 17 validate_forecast, forecast_validation
#> 14 prepare, preprocessing
#> 15 estimate_stability, bootstrap_stability
#> result_class available description
#> 10 net_gimme TRUE Group and individual uSEM path search
#> 4 gvar_result TRUE Sparse graphical VAR with EBIC selection
#> 5 gvar_list TRUE One graphical VAR per subject
#> 13 idioml_result TRUE Idiographic supervised machine learning
#> 6 net_mlvar TRUE Frequentist fixed-effects multilevel VAR
#> 7 net_mlvar_bayes TRUE Native Bayesian multilevel VAR/DSEM
#> 8 net_mplus TRUE Licensed Mplus-backed multilevel VAR
#> 12 rolling_gvar_result TRUE Rolling-window graphical VAR
#> 11 rolling_var_result TRUE Rolling-window ordinary VAR
#> 9 net_usem TRUE Person-specific unified SEM
#> 1 var_result TRUE Ordinary least-squares VAR(1)
#> 3 var_bayes_result TRUE Native Bayesian VAR(1)
#> 2 var_list TRUE One ordinary VAR per subject
#> 16 model_comparison TRUE Compare fitted idiographic network methods
#> 17 forecast_result TRUE Rolling-origin forecast validation
#> 14 preprocess_result TRUE Preprocessing and data-quality audit
#> 15 stability_result TRUE Edge-stability resampling workflow
list_estimators("workflow")
#> name kind function_name
#> 3 compare workflow compare_idiographic
#> 4 forecast workflow validate_forecast
#> 1 preprocess workflow preprocess
#> 2 stability workflow estimate_stability
#> aliases result_class available
#> 3 compare_idiographic, model_comparison model_comparison TRUE
#> 4 validate_forecast, forecast_validation forecast_result TRUE
#> 1 prepare, preprocessing preprocess_result TRUE
#> 2 estimate_stability, bootstrap_stability stability_result TRUE
#> description
#> 3 Compare fitted idiographic network methods
#> 4 Rolling-origin forecast validation
#> 1 Preprocessing and data-quality audit
#> 2 Edge-stability resampling workflow