Changes in version 0.1.2 (2026-10-28)                  

Bug fixes

  - Fixed D-vine conditional sampling for mechanisms with exactly 2
    parents (trivariate vine): replaced the rosenblatt() /
    inverse_rosenblatt() pipeline—whose behaviour changed for single-row
    matrices in newer rvinecopulib versions—with an explicit sequential
    h-function algorithm using pair_copulas(). Spearman correlations in
    the simulated output now correctly reflect the fitted dependence
    structure.

                        Changes in version 0.1.1                        

Bug fixes

  - Fixed copula_mechanism() with non-Gaussian families: hbicop() now
    returns a numeric vector in newer rvinecopulib versions; the
    bivariate path handles both vector and matrix return types.
  - Fixed D-vine conditional sampling for mechanisms with 2+ parents:
    unnamed matrices are now passed to rosenblatt() /
    inverse_rosenblatt() to prevent column reordering by name in newer
    rvinecopulib versions.
  - Removed reference to non-exported rvinecopulib::bicop_family_list in
    list_copula_families().

                        Changes in version 0.1.0                        

Initial release

  - Core system API: new_system(), add_feature(), add_dependency(),
    finalize_system().
  - Pre-built mechanism library covering all nine parent-type ×
    child-type combinations: linear_gaussian, multinomial_logit,
    ordered_logit, group_effect, categorical_transition, accessible via
    mechanism_library_get().
  - simulate() for population-level data generation under topological
    ordering with per-unit reproducible seeds.
  - Intervention types: do_fix(), do_distribution(), shift(),
    mechanism_override().
  - propagate() with mode = "full" (ATE / independent interventional
    draws) and mode = "partial" (ITE / frozen exogenous noise
    counterfactuals).
  - Vine copula mechanism via copula_mechanism(), backed by rvinecopulib
    (optional dependency).
  - Predictive model integration via model_mechanism() and
    learned_mechanism().
  - Language model mechanism via llm_mechanism().
  - JSON / YAML serialisation via export_system() / import_system().
  - DAG visualisation via plot_graph() (requires igraph).