impute_net: Elastic net imputation
In bcjaeger/midy: Imputation for Predictive Analytics

glmnet is used to impute missing values. For more details, see glmnet

impute_net(
  data_ref,
  data_new = NULL,
  fit = NULL,
  cols = dplyr::everything(),
  df_min = 1,
  df_max = 10,
  df_stp = 1,
  restore_data = TRUE,
  verbose = 1
)

`data_ref`	a data frame.
`data_new`	an optional data frame. If supplied, then `data_ref` will be used as a reference dataset for `data_new` and the output will contain imputed values for `data_new`. If not supplied, the output will contain imputed values for `data_ref`.
`fit`	a list of lists of strings that contain formulas from previously fitted imputation models. This input variable is created using `data_ref` when `data_new = NULL`.
`cols`	columns that should be imputed and/or used to impute other columns. Supports tidy select functions (see examples).
`df_min, df_max`	integer value designating the minimum and maximum degrees of freedom in penalized regression models.
`df_stp`	integer value indicating step size for model degrees of freedom between successive imputations.
`restore_data`	a logical value. If `TRUE`, the variable types of the imputed values will match those of the original data. If `FALSE`, the imputed values are returned in a one-hot encoded format.
`verbose`	an integer value of 0, 1, or 2. If `verbose = 0`, nothing is printed. If `verbose = 1`, messages are printed to the console showing what general steps are being taken in the imputation process. If `verbose = 2`, all relevant information on convergence is printed in addition to general messages.