generalize: Generalize Average Treatment Effect from Randomized Trial to...
In Generalizer/thegeneralizer:

Generalize Average Treatment Effect from Randomized Trial to Population

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generalize(outcome, treatment, trial, selection_covariates, data,
  method = "weighting", selection_method = "lr",
  is_data_disjoint = TRUE, trim_pop = FALSE, 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
`method`	method to generalize average treatment effect to the target population. Default is "weighting" (weighting by participation probability). Other methods supported are "BART" (Bayesian Additive Regression Trees - NOT READY YET) and "TMLE" (Targeted Maximum Likelihood Estimation)
`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.
`trim_pop`	logical. If TRUE, then population data are subset to exclude individuals with covariates outside bounds of trial covariates.
`seed`	numeric. By default, the seed is set to 13783, otherwise can be specified (such as for simulation purposes).