R: Weighting for Covariate Balance in Observational Studies

# Weighting for Covariate Balance in Observational Studies

## Documentation for package ‘WeightIt’ version 1.7.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

.weightit_methods | Weighting methods
anova.glm_weightit | Methods for 'glm_weightit()' objects
as.weightit | Create a 'weightit' object manually
as.weightit.default | Create a 'weightit' object manually
as.weightit.weightit.fit | Create a 'weightit' object manually
as.weightitMSM | Create a 'weightit' object manually
as.weightitMSM.default | Create a 'weightit' object manually
calibrate | Calibrate Propensity Score Weights
calibrate.default | Calibrate Propensity Score Weights
calibrate.weightit | Calibrate Propensity Score Weights
coxph_weightit | Fitting (Weighted) Cox Proportional Hazards Models
ESS | Compute effective sample size of weighted sample
estfun.glm_weightit | Methods for 'glm_weightit()' objects
get_w_from_ps | Compute weights from propensity scores
glm_weightit | Fitting (Weighted) Generalized Linear Models
glm_weightit-methods | Methods for 'glm_weightit()' objects
lm_weightit | Fitting (Weighted) Generalized Linear Models
make_full_rank | Make a design matrix full rank
method_bart | Propensity Score Weighting Using BART
method_cbps | Covariate Balancing Propensity Score Weighting
method_cfd | Characteristic Function Distance Balancing
method_ebal | Entropy Balancing
method_energy | Energy Balancing
method_entropy | Entropy 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 | Stable Balancing Weights
method_sbw | Stable Balancing Weights
method_super | Propensity Score Weighting Using SuperLearner
method_user | User-Defined Functions for Estimating Weights
msmdata | Simulated data for a 3 time point sequential study
multinom_weightit | Fitting (Weighted) Multinomial Regression Models
ordinal_weightit | Fitting (Weighted) Ordinal Regression Models
plot.summary.weightit | Print and Summarize Output
plot.summary.weightitMSM | Print and Summarize Output
plot.weightit | Plot information about the weight estimation process
predict.glm_weightit | Predictions for 'glm_weightit' objects
predict.multinom_weightit | Predictions for 'glm_weightit' objects
predict.ordinal_weightit | Predictions for 'glm_weightit' objects
print.glm_weightit | Methods for 'glm_weightit()' objects
sbps | Subgroup Balancing Propensity Score
summary.coxph_weightit | Methods for 'glm_weightit()' objects
summary.glm_weightit | Methods for 'glm_weightit()' objects
summary.multinom_weightit | Methods for 'glm_weightit()' objects
summary.ordinal_weightit | Methods for 'glm_weightit()' objects
summary.weightit | Print and Summarize Output
summary.weightitMSM | Print and Summarize Output
trim | Trim (Winsorize) Large Weights
trim.default | Trim (Winsorize) Large Weights
trim.weightit | Trim (Winsorize) Large Weights
update.glm_weightit | Methods for 'glm_weightit()' objects
vcov.glm_weightit | Methods for 'glm_weightit()' objects
weightit | Estimate Balancing Weights
weightit.fit | Generate Balancing Weights with Minimal Input Processing
weightitMSM | Generate Balancing Weights for Longitudinal Treatments