fit_growth.ModelStack: Fit Discrete Growth Curve SuperLearner
In osofr/growthcurveSL: SuperLearner for Imputing Growth Curves

Define and fit discrete SuperLearner for growth curve modeling. Model selection (scoring) can be based on either MSE for a single random (or last) holdout data-point for each subject (method = "holdout") or V-fold cross-validated MSE which uses entire subjects (entire growth curves) for model validation (method = "cv").

fit_growth(...)

## S3 method for class 'ModelStack'
fit_growth(models, method = c("none", "cv", "holdout",
  "holdout_cv", "SL"), data, ID, t_name, x, y, nfolds = NULL,
  fold_column = NULL, hold_column = NULL, hold_random = FALSE,
  seed = NULL, use_new_features = FALSE, refit = TRUE,
  verbose = getOption("gridisl.verbose"), ...)

`...`	Additional arguments that will be passed on to `gridisl::fit_model` function.
`models`	Parameters specifying the model(s) to fit. This must be a result of calling `gridisl::defModel(...) + gridisl::defModel(...)` functions. See `defModel` for additional information.
`method`	The type of model selection procedure when fitting several models. Possible options are "none" (no model selection), "holdout" – model selection based on validation holdout sample; "holdout_cv" – ; "cv" – model selection using V-fold cross-validation; "SL" – perform model stacking (combine all models) with Super Learner using V-fold cross-validation predictions.
`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).
`x`	A vector containing the names of predictor variables to use for modeling. If x is missing, then all columns except `ID`, `y` are used.
`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.
`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.
`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.
`seed`	Random number seed for selecting a random holdout.
`use_new_features`	Set to `TRUE` to use new features (predictors) defined by the growth curve feature-creator function `define_features_drop`. Note that the `define_features_drop` function is called automatically, but the features defined inside this function aren't use unless this is set to `TRUE`.
`refit`	Set to `TRUE` (default) to refit the best estimator using the entire dataset. When `FALSE`, it might be impossible to make predictions from this model fit.
`verbose`	Set to `TRUE` to print messages on status and information to the console. Turn this on by default using `options(gridisl.verbose=TRUE)`.