LorenzRegression: An Implementation of the Lorenz and Penalized Lorenz Regressions in R
(2026) Journal of Statistical Software — Vol. 117, n° 6, p. 1-33 (2026)
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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. In the context of a single-index model, these regression techniques estimate the explained Gini coefficient, a measure of inequality in the conditional expectation of the response given the covariates. 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 case, 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 functions and S3 methods are carefully described and their use is illustrated on income data.
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Citations
Jacquemain, A., & Heuchenne, C. (2026). LorenzRegression: An Implementation of the Lorenz and Penalized Lorenz Regressions in R. Journal of Statistical Software, 117(6), 1-33. https://doi.org/10.18637/jss.v117.i06 (Original work published 2026)