Function reference

weightit()
 Estimate Balancing Weights

weightitMSM()
 Generate Balancing Weights for Longitudinal Treatments

summary(<weightit>)
plot(<summary.weightit>)
summary(<weightitMSM>)
plot(<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
 Propensity Score Weighting Using Generalized Linear Models

method_ipt
 Inverse Probability Tilting

method_npcbps
 Nonparametric Covariate Balancing Propensity Score Weighting

method_optweight
method_sbw
 OptimizationBased Weighting

method_super
 Propensity Score Weighting Using SuperLearner

method_user
 UserDefined Functions for Estimating Weights

glm_weightit()
lm_weightit()
summary(<glm_weightit>)
 Fitting Weighted Generalized Linear Models

trim()
 Trim (Winsorize) Large Weights

calibrate()
 Calibrate Propensity Score Weights

sbps()
 Subgroup Balancing Propensity Score

get_w_from_ps()
 Compute weights from propensity scores

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

msmdata
 Simulated data for a 3 time point sequential study