idiographic: Person-Specific Statistics and Dynamic Networks

Person-specific and within-person analysis of intensive longitudinal and ESM panel data. The statistical workflow includes person-level descriptions (describe_persons()), centring, decomposition, and lagging (preprocess_panel()), scoped regression (fit_lm(), fit_glm()), machine learning (fit_ml()), within-between effects (fit_within_between()), coefficient pooling (pool_coefs()), subgroup analysis (test_subgroups(), find_subgroups()), treatment effects (fit_effects()), and heterogeneity (fit_heterogeneity()).

Details

Dynamic-network methods include network preprocessing audits (preprocess()), edge-stability diagnostics (estimate_stability()), rolling forecast validation (validate_forecast()), ordinary and graphical vector autoregression (fit_var(), fit_graphical_var()), multilevel and Bayesian VAR (fit_mlvar(), fit_mlvar_bayes()), unified SEM (fit_usem()), and GIMME (fit_gimme()). Use fit_idiographic() for registry-driven dispatch or the direct fit_*() functions. Results provide tidy accessors, readable print methods, diagnostics, and plots appropriate to their analytical level. equivalence() reports the exact validation scope attached to registered network methods.

See also

Useful links:

• https://pak.dynasite.org/idiographic/

• https://github.com/mohsaqr/idiographic

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

Author

Maintainer: Mohammed Saqr [email protected] [copyright holder]

Authors:

• Sonsoles López-Pernas [email protected]