Multivariate Quantile-on-Quantile Granger Causality R package mqqcause version 1.0.0 Implements bivariate and Multivariate Quantile-on-Quantile Granger causality tests building on the Quantile-on-Quantile regression framework of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger causality test of Troster (2018) <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates the local-linear slope in the quantile regression of y_t on lagged x_t with lagged y_t as control, using Gaussian kernel weights, and tests it against zero by paired bootstrap. The multivariate (conditional) test additionally conditions on a set of moderators Z and optional x times Z interaction terms, in the spirit of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the quantile grid is also provided. Heatmaps and 3D surfaces default to the 'MATLAB' 'Parula' colour map. Software Depends: R (>= 3.5.0) Imports: quantreg (>= 5.0), 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], Avik Sinha [ctb] (Original multivariate QQ causality methodology), Nicholas Sim [ctb] (Original QQ regression methodology), Hongtao Zhou [ctb] (Original QQ regression methodology) GPL-3 2026-06-01 application/tgz https://CRAN.R-project.org/package=mqqcause doi:10.32614/CRAN.package.mqqcause