R: Perform Causal Sensitivity Analyses Using Various Statistical Methods

# Perform Causal Sensitivity Analyses Using Various Statistical Methods

## Documentation for package ‘causens’ version 0.0.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

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.