Beyond Pareto: Bi-Objective and Multi-Objective Regression Trees’ R package BORT version 0.1.0 Implements the Bi-objective Regression Tree (BORT) for efficiently learning vector-valued functions. Unlike traditional methods that rely on constructing multiple models or static scalarisation, BORT integrates the exploration of the Pareto front directly into a single tree's growth process. It provides high-efficiency, single-model approaches that can Pareto-dominate entire Pareto-consistent families of trees, supported by a C backend for fast computation. For more details see Paz (2026) <doi:10.1007/978-3-032-28393-1_2> and Paz (2025) <doi:10.1007/978-3-031-78401-9_2>. Software Depends: R (>= 2.10.0) Erick G.G. de Paz <erick.giles@cimat.mx> Comprehensive R Archive Network (CRAN) Erick G.G. de Paz [aut, cre] (ORCID: <https://orcid.org/0000-0001-7878-8238>), Arturo Hernández-Aguirre [aut] (ORCID: <https://orcid.org/0000-0002-3744-9827>), Iván Cruz-Aceves [aut] (ORCID: <https://orcid.org/0000-0002-5197-2059>) GPL-2 2026-07-07 application/tgz https://CRAN.R-project.org/package=BORT doi:10.32614/CRAN.package.BORT