Quantile-on-Quantile Kernel Regularized Least Squares R package qqkrls version 1.0.0 Implements Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024) <doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014) <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile regression of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>: for each quantile theta of the independent variable the response is fit by KRLS on the corresponding sub-sample and the tau-quantile of the resulting pointwise marginal effects yields beta(theta, tau). Standard errors come from a paired bootstrap. Visualisations use the 'MATLAB' 'Parula' colour map by default. Software Depends: R (>= 3.5.0) Imports: KRLS (>= 1.0-0), plotly (>= 4.0.0), stats, utils, grDevices Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Merwan Roudane <merwanroudane920@gmail.com> Comprehensive R Archive Network (CRAN) Merwan Roudane [aut, cre, cph], Tomiwa Sunday Adebayo [ctb] (Original QQKRLS methodology), Jens Hainmueller [ctb] (Original KRLS methodology), Chad Hazlett [ctb] (Original KRLS methodology) GPL-3 2026-06-01 application/tgz https://CRAN.R-project.org/package=qqkrls doi:10.32614/CRAN.package.qqkrls