Man pages for WeightIt
Weighting for Covariate Balance in Observational Studies
| anova.glm_weightit | Methods for 'glm_weightit()' objects |
| as.weightit | Create a 'weightit' object manually |
| calibrate | Calibrate Propensity Score Weights |
| dot-weightit_methods | Weighting methods |
| ESS | Compute effective sample size of weighted sample |
| get_w_from_ps | Compute weights from propensity scores |
| glm_weightit | Fitting Weighted Generalized Linear Models |
| glm_weightit-methods | Methods for 'glm_weightit()' objects |
| make_full_rank | Make a design matrix full rank |
| 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 | Optimization-Based Weighting |
| 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 |
| plot.weightit | Plot information about the weight estimation process |
| predict.glm_weightit | Predictions for 'glm_weightit' objects |
| sbps | Subgroup Balancing Propensity Score |
| summary.weightit | Print and Summarize Output |
| trim | Trim (Winsorize) Large Weights |
| weightit | Estimate Balancing Weights |
| weightit.fit | Generate Balancing Weights with Minimal Input Processing |
| weightitMSM | Generate Balancing Weights for Longitudinal Treatments |
| WeightIt-package | WeightIt: Weighting for Covariate Balance in Observational... |
