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_ebcw

Empirical Balancing Calibration Weighting

method_energy

Energy Balancing

method_gbm

Propensity Score Weighting Using Generalized Boosted Models

method_npcbps

Nonparametric Covariate Balancing Propensity Score Weighting

method_optweight

Optimization-Based Weighting

method_ps

Propensity Score Weighting Using Generalized Linear Models

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 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