Changelog
Source:NEWS.md
moocore 0.1.9
CRAN release: 2025-11-01
-
hv_contributions()ignores dominated points by default. Setignore_dominated=FALSEto restore the previous behavior. The 3D case uses the HVC3D algorithm. - New function
any_dominated(). - New function
generate_ndset()to generate random nondominated sets with different shapes. - New article: “Benchmarks”
- New article: “Computing Multi-Objective Quality Metrics”
- New article: “Sampling Random Nondominated Sets”
-
is_nondominated(),any_dominated()andpareto_rank()now handle single-objective inputs correctly (#27) (#29). -
is_nondominated()andfilter_dominated()are faster for dimensions larger than 3. -
is_nondominated()andfilter_dominated()are now stable in 2D and 3D withkeep_weakly=FALSE, that is, only the first of duplicated points is marked as nondominated.
moocore 0.1.8
CRAN release: 2025-07-15
- Document the EAF and Vorob’ev expectation and deviation in more detail.
- New function
hv_approx(). - Function
hv_contributions()is much faster for 2D inputs. - New article “Approximating the hypervolume”.
- New datasets
DTLZLinearShape.8d.front.60pts.10andran.10pts.9d.10.
moocore 0.1.7
CRAN release: 2025-06-05
hypervolume()now uses the HV3D+ algorithm for the 3D case and the HV4D+ algorithm for the 4D case. For dimensions larger than 4, the recursive algorithm uses HV4D+ as the base case, which is significantly faster.read_datasets()is significantly faster for large files.is_nondominated()andfilter_dominated()are faster for 3D inputs.
moocore 0.1.5
CRAN release: 2025-05-11
- Rename
vorobT()andvorobDev()tovorob_t()andvorob_dev()to be consistent with other function names.