*! version 0.0.2 23feb2024

In this phase, your estimation command is implemented to fit the model you
specified to one or more folds of the data in the training set. The commands in
the crossvalidate package determine whether your estimation command
includes any [if] [in] expressions. If your estimation command includes an in
expression, this command create a temporary variable indicating the cases
that satisfy your in expression. The in expression is then replaced by an if
expression using that temporary variable indicator and an additional constraint
to ensure that the model is fitted to the correct fold of the training set.
If your estimation command includes an if expression, we add the additional
constraint to fit the model to the correct fold to the expression. If your
estimation command does not include an [if] [in] expression, we inject an if
expression to ensure that the model is fitted to the correct fold of the
training set.

The model is then fitted to all of the training folds sequentially based on the
value provided to the kfold option. This option defaults to a value of 1.
If you are using one of the prefix commands (xv or xvloo), you
do not need to specify the kfold option more than once. The
estimation results are stored for each iteration and you are required to provide
a stubname for the estimation results; Note: if you are using one
of the prefix commands (xv or xvloo), a default name is
provided as a convenience. The name you use for the estimation results
CAN NOT end with a number. When predicting the outcomes from the models,
a scenario can arise where it is no longer possible to identify the correct
estimation result using the fold identifier if the name supplied ends with a
number.

In the case where you are attempting to tune the hyperparameters or select
among competing models, you may want to use results from the validation set of
a TVT split. The noall option can be useful in this scenario. By default
the command will fit the model to each fold of the training set and then will
fit the model to the entire training set, when kfold is > 1. When you
plan on testing multiple models or performing hyperparameter tuning, use the
noall option while engaged in this preliminary work. Once you have a
candidate model, call the command again without the noall option. This
will fit the model on the entire training set and that model fit will be used to
generate the predicted values on the validation set.