SLfit_wrapper: Wrapper for growthcurve SL function calls.
In osofr/growthcurveSL: SuperLearner for Imputing Growth Curves

Predefine a wrapper for model fitting. This function returns a function that takes in two arguments: models and x. See the arguments models and x in fit_growth function for additional details.

1 2	SLfit_wrapper(method, fold_column = NULL, hold_column = NULL, data, ID, t_name, y)

`method`	The type of model selection procedure when fitting several models. Possible options are "none" (no model selection), "cv" (model selection with V-fold cross-validation), and "holdout" (model selection based on validation holdout sample).
`fold_column`	The name of the column in the input data that contains the cross-validation fold indicators (must be an ordered factor).
`hold_column`	The name of the column that contains the holdout observation indicators (TRUE/FALSE) in the input data. This holdout column must be defined and added to the input data prior to calling this function.
`data`	Input dataset, can be a `data.frame` or a `data.table`.
`ID`	A character string name of the column that contains the unique subject identifiers.
`t_name`	A character string name of the column with integer-valued measurement time-points (in days, weeks, months, etc).
`y`	A character string name of the column that represent the response variable in the model.
`nfolds`	Number of folds to use in cross-validation.
`hold_random`	Logical, specifying if the holdout observations should be selected at random. If FALSE then the last observation for each subject is selected as a holdout.