weighting: Estimate weights for generalizing ATE by predicting...
In Generalizer/thegeneralizer:

Estimate weights for generalizing ATE by predicting probability of trial participation

1 2	weighting(outcome, treatment, trial, selection_covariates, data, selection_method = "lr", is_data_disjoint = TRUE, seed)

`outcome`	variable name denoting outcome
`treatment`	variable name denoting binary treatment assignment (ok if only available in trial, not population)
`trial`	variable name denoting binary trial participation (1 = trial participant, 0 = not trial participant)
`selection_covariates`	vector of covariate names in data set that predict trial participation
`data`	data frame comprised of "stacked" trial and target population data
`selection_method`	method to estimate the probability of trial participation. Default is logistic regression ("lr"). Other methods supported are Random Forests ("rf") and Lasso ("lasso")
`is_data_disjoint`	logical. If TRUE, then trial and population data are considered independent. This affects calculation of the weights - see details for more information.
`seed`	numeric. By default, the seed is set to 13783, otherwise can be specified (such as for simulation purposes).