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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.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.rosenblatt() /
inverse_rosenblatt() to prevent column reordering by name
in newer rvinecopulib versions.rvinecopulib::bicop_family_list in
list_copula_families().new_system(),
add_feature(), add_dependency(),
finalize_system().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.do_fix(),
do_distribution(), shift(),
mechanism_override().propagate() with mode = "full" (ATE /
independent interventional draws) and mode = "partial" (ITE
/ frozen exogenous noise counterfactuals).copula_mechanism(), backed by
rvinecopulib (optional dependency).model_mechanism() and
learned_mechanism().llm_mechanism().export_system() /
import_system().plot_graph() (requires
igraph).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.