Estimates the reduced dimension regressions necessary for the additional fluctuations.
estimategrn(Y, A, W, DeltaA, DeltaY, Qn, gn, SL_gr, tolg, glm_gr, a_0,
reduction, returnModels, validRows)A vector of continuous or binary outcomes.
A vector of binary treatment assignment (assumed to be equal to 0 or 1).
A data.frame of named covariates.
Indicator of missing treatment (assumed to be equal to 0 if missing 1 if observed).
Indicator of missing outcome (assumed to be equal to 0 if missing 1 if observed).
A list of outcome regression estimates evaluated on observed data.
A list of propensity regression estimates evaluated on observed data.
A vector of characters or a list describing the Super Learner library to be used for the reduced-dimension propensity score.
A numeric indicating the minimum value for estimates of the propensity score.
A character describing a formula to be used in the call to
glm for the second reduced-dimension regression. Ignored if
SL_gr!=NULL.
A list of fixed treatment values .
A character equal to 'univariate' for a univariate
misspecification correction or 'bivariate' for the bivariate version.
A boolean indicating whether to return model fits for the outcome regression, propensity score, and reduced-dimension regressions.
A list of length cvFolds containing the row
indexes of observations to include in validation fold.