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cff-version: 1.2.0
message: 'To cite package "rifexpectile" in publications use:'
type: software
license: MIT
title: 'rifexpectile: Density-Free RIF Decompositions for Unconditional Expectiles'
version: 0.1.1
doi: 10.32614/CRAN.package.rifexpectile
abstract: Implements a density-free recentered influence function (RIF) regression
  framework for unconditional expectiles, and embeds it in a two-sample Oaxaca-Blinder
  decomposition indexed continuously by the expectile level. Unlike quantile-based
  RIF decompositions, which require estimating an inverse density term at each quantile,
  the expectile RIF depends only on primitive moments of the outcome distribution
  and requires no density estimation, no bandwidth selection, and no kernel smoothing.
  The package provides expectile estimation by iteratively reweighted least squares,
  closed-form RIF construction, two-sample composition/structure decomposition across
  a grid of expectile levels, bootstrap-based inference, and plotting methods. The
  underlying methodology is described in Ndoye (2025), "Semi-Nonparametric Expectile
  RIF Regression for Distributional Decomposition," presented at the 2025 World Congress
  of the Econometric Society, Seoul, Korea, <https://www.econometricsociety.org/regional-activities/conference-papers/view/282/943>.
authors:
- family-names: Ndoye
  given-names: Abdoul Aziz
  email: abdoulaziz1.ndoye@gmail.com
repository: https://aandoye.r-universe.dev
repository-code: https://github.com/aandoye/rifexpectile
commit: 355f9acb2393615c539fc0aaed427a21cfb38ffe
url: https://github.com/aandoye/rifexpectile
date-released: '2026-08-31'
contact:
- family-names: Ndoye
  given-names: Abdoul Aziz
  email: abdoulaziz1.ndoye@gmail.com