bayesian_causens        Bayesian parametric sensitivity analysis for
                        causal inference
causens_monte_carlo     Monte Carlo sensitivity analysis for causal
                        effects
causens_sf              Bayesian Estimation of ATE Subject to
                        Unmeasured Confounding
create_jags_model       Create an JAGS model for Bayesian sensitivity
                        analysis
gData_U_binary_Y_binary
                        Generate data with a binary unmeasured
                        confounder and binary outcome
gData_U_binary_Y_cont   Generate data with a binary unmeasured
                        confounder and continuous outcome
gData_U_cont_Y_binary   Generate data with a continuous unmeasured
                        confounder and a binary outcome
gData_U_cont_Y_cont     Generate data with a continuous unmeasured
                        confounder and continuous outcome
plot_causens            Plot ATE with respect to sensitivity function
                        value when it is constant, i.e. c(1, e) = c1
                        and c(0, e) = c0.
process_model_formula   Process model formula
sf                      Calculate sensitivity of treatment effect
                        estimate to unmeasured confounding
simulate_data           Generate data with unmeasured confounder
summary.bayesian_causens
                        Summarize the results of a causal sensitivity
                        analysis via Bayesian modelling of an
                        unmeasured confounder.
summary.causens_sf      Summarize the results of a causal sensitivity
                        analysis via sensitivity function.
summary.monte_carlo_causens
                        Summarize the results of a causal sensitivity
                        analysis via the Monte Carlo method.
