Package index

Fitting

Fit the BIc model and evaluate its fitted parts.

bic_fit()

Fit a BIc (zero-or-one inflated beta) regression

bic_parts()

Fitted BIc component parts under a counterfactual treatment value

Identification and estimation

G-computation estimate of the average/conditional treatment effect and its exact discrete/continuous decomposition. This is the layer that identifies the effect; nothing below it changes this result.

bic_gcomp()

G-computation decomposition of the average treatment effect

bic_cate()

Conditional average treatment effect at covariate profiles

Diagnostics

The causal-perturbation diagnostic layer: curvature, per-unit influence, and sensitivity to unmeasured confounding.

bic_curvature()

Causal conformal normal curvature (CCNC) and its block decomposition

bic_icim()

Individual Causal Influence Measure (ICIM)

bic_sensitivity()

Sensitivity of the average treatment effect to unmeasured confounding

Simulation

simulate_bic()

Simulate data from a causal zero-inflated beta (BIc) model

Figures

Reproduce the manuscript’s figures from the package alone.

plot_concept()

The curvature concept figure (Figure 1 of Ospina, 2026)

plot_cate()

The conditional average treatment effect figure (Figure 2 of Ospina, 2026)

plot_diagnostics()

Goodness-of-fit diagnostics for a fitted BIc model (Figure 5 of Ospina, 2026)