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

The package lassopmm

lassopmm(source, target, dep_var, indep_var, weight_var = NULL,
  extra_var = NULL, strata_vars = NULL, cluster_vars = NULL,
  n_near = 10, n_boot = 5, force_boot = NULL, force_lambda = NULL,
  n_folds = 10, reduced = TRUE, updateProgress = NULL)

`source, target`	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`	character vectors. Could be `NULL`, contain one element or a vector of multiple elements. `extra_var` represents names of the variable, which should be joint from the `source` data to the `target` data based on the match by the dependent variable `dep_var`.
`strata_vars, cluster_vars`	character vectors. Could be `NULL`, contain one element or a vector of multiple elements. `strata_vars` exist for using stratified sampling in the bootstrapping process. `cluster_vars` used for clastered sampling in the bootstrapping process. Any combinations of variables could be used.
`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 bootstrap permulations (resamplings). If `n_rep = 0`, no bootstraping is performed and we have the bootstrap sample, whcih consist of the same observations in the same order as source data.
`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.
`force_lambda`	allows to specify one lamda value. Shoul be 0, when we want to switch to the linear regression.
`n_folds`	number of folds for cross-validation
`reduced`	if TRUE terurns reduced outpur without specific regression details.
`updateProgress`	function that updates the progress bars in the shiny app.