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Abstract
Lorenz regressions are statistical tools for measuring the extent of inequality in a response variable that is attributable to a set of covariates. These regression techniques estimate the explained Gini coefficient, a measure of inequality in the conditional expectation of the response given the covariates, assuming a single-index model. In this paper, we describe the LorenzRegression package, which implements the non-penalized and penalized Lorenz regressions. The non-penalized procedure is implemented via a genetic algorithm, making use of the GA package. For the penalized case, the user can choose between using a lasso or SCAD penalty. In the former, the estimation procedure is performed with the FABS algorithm, while the latter is implemented with the SCAD-FABS algorithm. Computationally intensive parts of the code are written in C++. A mathematical description of the procedure is provided. The main function is carefully described and its use is illustrated on income data.
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Jacquemain, A., & Heuchenne, C. (2023). Lorenz Regression: an implementation of the Lorenz and penalized Lorenz regressions in R (LIDAM Discussion Paper ISBA 2023/27). https://hdl.handle.net/2078.5/235144