Working on our previous contributions for JuliaCon 2018 (see GlobalSearchRegresssion.jl, GlobalSearchRegressionGUI.jl, and [our JuliaCon 2018 Lighting Talk] (https://bit.ly/2UC7dr1)) we develop a new GlobalSearchRegression.jl version merging LASSO and QR-OLS algorithms, and including new outcome capabilities. Combining machine learning (ML) and econometric (EC) procedures allows us to deal with a much larger set of potential covariates (e.g. from 30 to hundresds) preserving most of the original advantages of all-subset regression approaches (in-sample and out-of sample optimality, model averaging results and residuals tests for coefficient robustness). Additionally, the new version of GlobalSearchRegression.jl allows users to obtain LATEX and PDF outcomes with best model results, model averaging estimations and key statistics distributions