Compute data-adaptive parameter estimate for a single cross-validation fold

cv_param_est(fold, data, parameter_wrapper, absolute, negative, n_top,
  learning_library, Y_name, A_name, W_name)

Arguments

fold	fold output from `origami`
data	entire training data
parameter_wrapper	user-defined function
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
n_top	integer value for the number of candidate covariates to generate using the data-adaptive estimation algorithm
learning_library	character of `SuperLearner` library
Y_name	(character) `colnames` of all biomarkers
A_name	(character) `colnames` of treatment
W_name	(character) `colnames` of all baseline covariates

data_adaptive_index (integer vector) rank for each gene

index_grid (integer matrix) gene index from rank 1 to rank K

psi_est estimand of DE for rank 1 to rank K genes

EIC_est estimand of EIC for rank 1 to rank K genes