Package index
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bayesian_causens() - Bayesian parametric sensitivity analysis for causal inference
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causens_monte_carlo() - Monte Carlo sensitivity analysis for causal effects
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causens_sf() - Bayesian Estimation of ATE Subject to Unmeasured Confounding
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create_jags_model() - Create an JAGS model for Bayesian sensitivity analysis
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gData_U_binary_Y_binary() - Generate data with a binary unmeasured confounder and binary outcome
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gData_U_binary_Y_cont() - Generate data with a binary unmeasured confounder and continuous outcome
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gData_U_cont_Y_binary() - Generate data with a continuous unmeasured confounder and a binary outcome
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gData_U_cont_Y_cont() - Generate data with a continuous unmeasured confounder and continuous outcome
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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.
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process_model_formula() - Process model formula
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sf() - Calculate sensitivity of treatment effect estimate to unmeasured confounding
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simulate_data() - Generate data with unmeasured confounder
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summary(<bayesian_causens>) - Summarize the results of a causal sensitivity analysis via Bayesian modelling of an unmeasured confounder.
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summary(<causens_sf>) - Summarize the results of a causal sensitivity analysis via sensitivity function.
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summary(<monte_carlo_causens>) - Summarize the results of a causal sensitivity analysis via the Monte Carlo method.