Function reference

weightit()
print(<weightit>)
 Generate Balancing Weights

weightitMSM()
print(<weightitMSM>)
 Generate Balancing Weights for Longitudinal Treatments

summary(<weightit>)
print(<summary.weightit>)
plot(<summary.weightit>)
summary(<weightitMSM>)
print(<summary.weightitMSM>)
 Print and Summarize Output

method_bart
 Propensity Score Weighting Using BART

method_cbps
 Covariate Balancing Propensity Score Weighting

method_ebal
 Entropy Balancing

method_energy
 Energy Balancing

method_gbm
 Propensity Score Weighting Using Generalized Boosted Models

method_glm
method_ps
 Propensity Score Weighting Using Generalized Linear Models

method_npcbps
 Nonparametric Covariate Balancing Propensity Score Weighting

method_optweight
 OptimizationBased Weighting

method_super
 Propensity Score Weighting Using SuperLearner

method_user
 UserDefined Functions for Estimating Weights

stop.method
 Balance criteria for tuning

get_w_from_ps()
 Compute weights from propensity scores

trim(<weightit>)
trim(<numeric>)
 Trim (Winsorize) Large Weights

sbps()
print(<weightit.sbps>)
summary(<weightit.sbps>)
print(<summary.weightit.sbps>)
 Subgroup Balancing Propensity Score

ESS()
 Compute effective sample size of weighted sample

weightit.fit()
 Generate Balancing Weights with Minimal Input Processing

as.weightit()
as.weightitMSM()
 Create a
weightit
object manually

make_full_rank()
 Make a design matrix full rank