moocore 0.1.9

• hv_contributions() ignores dominated points by default. Set ignore_dominated=FALSE to 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() and pareto_rank() now handle single-objective inputs correctly (#27) (#29).
• is_nondominated() and filter_dominated() are faster for dimensions larger than 3.
• is_nondominated() and filter_dominated() are now stable in 2D and 3D with keep_weakly=FALSE, that is, only the first of duplicated points is marked as nondominated.

moocore 0.1.8

• 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.10 and ran.10pts.9d.10.

moocore 0.1.7

• 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() and filter_dominated() are faster for 3D inputs.

moocore 0.1.6

• Fix parallel build in CRAN.

moocore 0.1.5

• Rename vorobT() and vorobDev() to vorob_t() and vorob_dev() to be consistent with other function names.

moocore 0.1.2

• Fix more warnings and problems that only show in CRAN.

moocore 0.1.1

• Fix problems that only show in CRAN.

moocore 0.1.0

• Initial version uploaded to CRAN.