adaptest_old: OLD Data-Adaptive Algorithm Implementation (for reference...
In adaptest: Data-Adaptive Statistics for High-Dimensional Multiple Testing

Description Usage Arguments Value Author(s)

Performs targeted minimum loss-based estimation (TMLE )of a marginal additive treatment effect of a binary point treatment on an outcome. The data-adaptive algorithm is used to perform variable reduction to avoid the disadvantages associated with multiple testing. INTERNAL USE ONLY.

adaptest_old(Y, A, W = NULL, n_top, n_fold, folds_vec = NULL,
  parameter_wrapper = adaptest::rank_DE, learning_library = c("SL.glm",
  "SL.step", "SL.glm.interaction", "SL.gam", "SL.earth"),
  absolute = FALSE, negative = FALSE)

`Y`	continuous or binary outcome variable
`A`	binary treatment indicator: `1` = treatment, `0` = control
`W`	matrix containing baseline covariates
`n_top`	integer value for the number of candidate covariates to generate using the data-adaptive estimation algorithm
`n_fold`	integer number of folds to be used for cross-validation
`folds_vec`	Vector of `numeric` indicating the validation fold in which a given observation falls for a standard V-fold cross-validation procedure. Default is `NULL`, in which case a custom cross-validation procedure is used.
`parameter_wrapper`	function
`learning_library`	character
`absolute`	boolean: `TRUE` = test for absolute effect size. This `FALSE` = test for directional effect. This overrides argument `negative`.
`negative`	boolean: `TRUE` = test for negative effect size, `FALSE` = test for positive effect size