moocore 0.3.1

• Separate documentation of pareto_rank() to its own page.
• Fix potential undefined behavior in is_nondominated().

moocore 0.3.0

• r2_exact() implements the exact computation of the R2 indicator for bi-objective solution sets.
• hypervolume() is significantly faster for more than four dimensions (Andreia P. Guerreiro).
• hypervolume() now handles 1D inputs and provides a clear error for 0D inputs (#58).
• Add GPGame and targeted packages to Benchmarks.
• is_nondominated(), filter_dominated() and pareto_rank() use a significantly faster algorithm by Kung et al. for dimensions larger than 3.
• generate_ndset(): New shapes "inverted-simplex" and "concave-simplex". Shape "convex-simplex" is now equivalent to generate_ndset(..., method="simplex")^2, which is slightly more uniform than the previous approach.
• The default method of hv_approx() is now "Rphi-FWE+", which is typically as accurate as the other methods, but significantly faster.

moocore 0.2.0

• pareto_rank() is faster in 3D.
• HV3D+ is slightly faster with repeated coordinates (#41).
• igd(), igd_plus(), avg_hausdorff_dist() are faster.
• Fix wrong values returned by epsilon_mult() when mixing minimization and maximization.
• Fix documentation of epsilon_additive() (@leandrolanzieri)
• hypervolume() uses the inclusion-exclusion algorithm for small inputs of up to 15 points, which is significantly faster.

moocore 0.1.10

• mooocore now requires R >= 4.1.
• is_nondominated(): Fix wrong assert (#38).

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.