Lorenz and Penalized Lorenz Regressions

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) . The implementation is described in Jacquemain and Heuchenne (2026) .

LorenzRegression

The LorenzRegression package proposes a toolbox to estimate, produce inference on and interpret Lorenz regressions. These regressions are used to determine the explanatory power of a set of covariates on the inequality of a response variable.

Installation

You can install the released version of LorenzRegression from CRAN with:

install.packages("LorenzRegression")

You can install the development version of this package from GitHub with:

# install.packages("devtools")
devtools::install_github("AlJacq/LorenzRegression")

What’s new

LorenzRegression 2.0.0

• Function Structure Overhaul: Lorenz.Reg now acts as a wrapper for fitting functions Lorenz.GA, Lorenz.FABS, and Lorenz.SCADFABS, returning objects of class "LR" or "PLR" with designated methods.

• Enhanced Bootstrap and CV: Lorenz.boot performs bootstrap calculations and out-of-bag score computation, while PLR.CV handles cross-validation for tuning parameter selection.

• Methods: New methods fitted, explainedIneq, and autoplot for "LR" and "PLR" objects. Method availability is documented in the Lorenz.Reg help page with individual help pages for each method.