https://depazcimat.r-universe.devRecent package updates in depazcimatR-universehttps://github.com/depazcimat.pnghttps://depazcimat.r-universe.devTue, 07 Jul 2026 09:50:08 GMTerick.giles@cimat.mx (Erick G.G. de Paz)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>.https://github.com/r-universe/depazcimat/actions/runs/28923178097Tue, 07 Jul 2026 09:50:08 GMTBORT0.1.0successhttps://depazcimat.r-universe.devhttps://github.com/cran/BORT