| chebyshevAssociationBound(R1ToR1, boolean, R1ToR1, boolean) |  | 0% | | 0% | 5 | 5 | 13 | 13 | 1 | 1 |
| empiricalAnchorMoment(int, double, boolean) | | 0% | | 0% | 5 | 5 | 8 | 8 | 1 | 1 |
| SingleSequenceAgnosticMetrics(double[], R1Univariate) |  | 88% | | 75% | 3 | 7 | 3 | 24 | 0 | 1 |
| markovUpperProbabilityBound(double, R1ToR1) | | 79% | | 64% | 5 | 8 | 2 | 11 | 0 | 1 |
| weakLawAverageBounds(double) | | 82% | | 62% | 3 | 5 | 3 | 9 | 0 | 1 |
| chebyshevBound(double) | | 80% | | 62% | 3 | 5 | 3 | 8 | 0 | 1 |
| chebyshevCantelliBound(double) | | 76% | | 50% | 3 | 4 | 3 | 7 | 0 | 1 |
| centralMomentBound(double, int) | | 82% | | 66% | 2 | 4 | 3 | 8 | 0 | 1 |
| functionSequenceMetrics(R1ToR1) | | 82% | | 75% | 1 | 3 | 3 | 9 | 0 | 1 |
| empiricalCentralMoment(int, boolean) | | 86% | | 66% | 2 | 4 | 1 | 8 | 0 | 1 |
| empiricalRawMoment(int, boolean) | | 88% | | 83% | 1 | 4 | 1 | 7 | 0 | 1 |
| populationDistribution() | | 0% | | n/a | 1 | 1 | 1 | 1 | 1 | 1 |
| populationMean() | | 100% | | 100% | 0 | 2 | 0 | 1 | 0 | 1 |
| populationVariance() | | 100% | | 100% | 0 | 2 | 0 | 1 | 0 | 1 |
| empiricalExpectation() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| empiricalVariance() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| isPositive() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| sequence() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |