estimate_G: estimateG
In benkeser/intermed:

Function to estimate propensity score

estimate_G(
  A,
  W,
  DeltaY,
  DeltaA,
  SL_g,
  glm_g,
  a_0,
  tolg,
  stratify = FALSE,
  validRows = NULL,
  verbose = FALSE,
  returnModels = FALSE,
  Qn = NULL,
  adapt_g = FALSE
)

`A`	A vector of binary treatment assignment (assumed to be equal to 0 or 1)
`W`	A `data.frame` of named covariates
`DeltaY`	Indicator of missing outcome (assumed to be equal to 0 if missing 1 if observed)
`DeltaA`	Indicator of missing treatment (assumed to be equal to 0 if missing 1 if observed)
`SL_g`	A vector of characters describing the super learner library to be used for each of the regression (`DeltaA`, `A`, and `DeltaY`). To use the same regression for each of the regressions (or if there is no missing data in `A` nor `Y`), a single library may be input.
`glm_g`	A character describing a formula to be used in the call to `glm` for the propensity score.
`a_0`	A vector of fixed treatment values at which to return marginal mean estimates.
`tolg`	A numeric indicating the minimum value for estimates of the propensity score.
`stratify`	A `boolean` indicating whether to estimate the missing outcome regression separately for observations with `A` equal to 0/1 (if `TRUE`) or to pool across `A` (if `FALSE`).
`validRows`	A `list` of length `cvFolds` containing the row indexes of observations to include in validation fold.
`verbose`	A boolean indicating whether to print status updates.
`returnModels`	A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduced-dimension regressions.
`Qn`	A `list` of estimates of the outcome regression for each value in `a_0`. Only needed if `adapt_g = TRUE`.
`adapt_g`	A boolean indicating whether propensity score is adaptive to outcome regression.