impute_data: Impute data to from the period 0 dataframe to the period 1...
In EBukin/syntheticpanel: Create synthetic panels from cross sectional data

Impute data to from the period 0 dataframe to the period 1 using "lassopmm" logic.

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impute_data(period_0, period_1, dep_var, indep_var, weight_var = NULL,
  extra_var = NULL, n_near = 10, n_boot = 5, group_boot_var = NULL,
  force_boot = NULL, n_folds = 10, reduced = TRUE)

`period_0, period_1`	dataframes with the period 0 and period 1 data respectively. These dataframes have to contain same columns with the identical names and variable types. If this is violated, errors may appear in the process of running the function.
`dep_var`	character vector with one name of the dependent variable.
`indep_var`	character vector with the names of independent variables.
`weight_var`	character vector with one name of the weight variable. Default is 'NULL', when 'NULL' equall weights of 1 for each obesrcation are assumed.
`extra_var, group_boot_var`	character vectors. Could be `NULL`, contain one element or a vector of multiple lements. `extra_var` represents names of the variable, which should be joint from the `period_0` data to the `period_1` data based on the match by the dependent variable `dep_var`.
`n_near`	number of the nearest observations to derive a random match. If 'n_near' is greater than 'length(match_vector)', minimum out of two is used to create a sample for selecting a random match value.
`n_boot`	number of bootsrtapping iterations.
`force_boot`	bootstrapping permutation vector externally defined. Default is `NULL`. Has to be provided a dataframe, where each column represent indexes of the resampled observations for each bootsrtap iteration.
`n_folds`	number of folds for cross-validation
`reduced`	if TRUE terurns reduced outpur without specific regression details.