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Estimate weights

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

Specific estimation methods

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
Optimization-Based Weighting
method_super
Propensity Score Weighting Using SuperLearner
method_user
User-Defined Functions for Estimating Weights

Other functions

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