R: Causal Inference for Multiple Treatments with a Binary Outcome

# Causal Inference for Multiple Treatments with a Binary Outcome

## Documentation for package ‘CIMTx’ version 1.2.0

- DESCRIPTION file

## Help Pages

ce_estimate | Causal inference with multiple treatments using observational data
ce_estimate_bart_ate | Causal inference with multiple treatments using BART for ATE effects
ce_estimate_bart_att | Causal inference with multiple treatments using BART for ATT effects
ce_estimate_iptw_ate | Causal inference with multiple treatments using IPTW for ATE effects
ce_estimate_iptw_ate_boot | Causal inference with multiple treatments using IPTW for ATE effects (bootstrapping for CI)
ce_estimate_iptw_att | Causal inference with multiple treatments using IPTW for ATT effects
ce_estimate_iptw_att_boot | Causal inference with multiple treatments using IPTW for ATT effects (bootstrapping for CI)
ce_estimate_rams_ate | Causal inference with multiple treatments using RAMS for ATE effects
ce_estimate_rams_ate_boot | Causal inference with multiple treatments using RAMS for ATE effects (bootstrapping for CI)
ce_estimate_rams_att | Causal inference with multiple treatments using RAMS for ATT effects
ce_estimate_rams_att_boot | Causal inference with multiple treatments using RAMS for ATT effects (bootstrapping for CI)
ce_estimate_ra_ate | Causal inference with multiple treatments using RA for ATE effects
ce_estimate_ra_att | Causal inference with multiple treatments using RA for ATT effects
ce_estimate_tmle_ate | Causal inference with multiple treatments using TMLE for ATE effects
ce_estimate_tmle_ate_boot | Causal inference with multiple treatments using TMLE for ATE effects (bootstrapping for CI)
ce_estimate_vm_att | Causal inference with multiple treatments using VM for ATT effects
covariate_overlap | Covariate overlap figure
data_sim | Simulate data for binary outcome with multiple treatments
plot.CIMTx_ATE_posterior | Plot for non-IPTW estimation methods with bootstrapping for ATE effect
plot.CIMTx_ATT_posterior | Plot for non-IPTW estimation methods for ATT effect
plot.CIMTx_IPTW | Boxplot for weight distribution
plot.CIMTx_nonIPTW_once | Plot for non-IPTW estimation methods for ATE effect
plot.CIMTx_sa_grid | Contour plot for the grid specification of sensitivity analysis
posterior_summary | Posterior distribution summary
print.CIMTx_ATE_posterior | Print the ATE results for non-IPTW results
print.CIMTx_ATE_sa | Print the ATE results for from sensitivity analysis
print.CIMTx_ATT_posterior | Print the ATT results
print.CIMTx_ATT_sa | Print the ATT results for from sensitivity analysis
print.CIMTx_IPTW | Print the ATE/ATT results for IPTW results
print.CIMTx_nonIPTW_once | Print the ATE/ATT results for non-IPTW results
print.CIMTx_sa_grid | Print the ATT results for from sensitivity analysis
sa | Flexible Monte Carlo sensitivity analysis for unmeasured confounding
summary.CIMTx_ATE_posterior | Summarize a CIMTx_ATE_posterior object
summary.CIMTx_ATE_sa | Summarize a CIMTx_ATE_sa object
summary.CIMTx_ATT_posterior | Summarize a CIMTx_ATT_posterior object
summary.CIMTx_ATT_sa | Summarize a CIMTx_ATT_sa object
summary.CIMTx_IPTW | Summarize a CIMTx_IPTW object
summary.CIMTx_nonIPTW_once | Summarize a CIMTx_nonIPTW_once object
true_c_fun_cal | Calculate the true c functions with 3 treatments and a binary predictor
trunc_fun | Trimming