| setCovariance(Matrix, boolean) |   | 85% | | 100% | 0 | 4 | 2 | 14 | 0 | 1 |
| MultivariateNormalDist(int) | | 74% | | 100% | 0 | 2 | 2 | 9 | 0 | 1 |
| processCovariance() | | 83% | | 66% | 2 | 4 | 1 | 8 | 0 | 1 |
| invcdf(double[], Matrix) | | 75% | | 50% | 1 | 2 | 1 | 5 | 0 | 1 |
| invcdf(double, Matrix) | | 75% | | 50% | 1 | 2 | 1 | 5 | 0 | 1 |
| invcdf(double[], double[], Matrix) | | 95% | | 100% | 0 | 6 | 3 | 21 | 0 | 1 |
| cdf(double[], Matrix) | | 94% | | 100% | 0 | 5 | 3 | 19 | 0 | 1 |
| isValidCovariance(Matrix) | | 85% | | 100% | 0 | 2 | 2 | 7 | 0 | 1 |
| p(double[]) | | 97% | | 100% | 0 | 3 | 1 | 11 | 0 | 1 |
| squaredMahalanobisDistance(double[]) | | 96% | | n/a | 0 | 1 | 1 | 9 | 0 | 1 |
| propagate(MultivariateNormalDist.JacobianEvaluator, MultivariateNormalDist, MultivariateNormalDist) | | 90% | | n/a | 0 | 1 | 1 | 4 | 0 | 1 |
| invcdf(double, double[], Matrix) | | 100% | | 100% | 0 | 5 | 0 | 11 | 0 | 1 |
| propagate(MultivariateNormalDist.JacobianEvaluator, double[], Matrix, MultivariateNormalDist) | | 100% | | n/a | 0 | 1 | 0 | 12 | 0 | 1 |
| jointProbability(double[]) | | 100% | | 100% | 0 | 2 | 0 | 4 | 0 | 1 |
| setMeanAndCovariance(double[], Matrix, boolean) | | 100% | | 100% | 0 | 2 | 0 | 5 | 0 | 1 |
| isReady() | | 100% | | 66% | 2 | 4 | 0 | 2 | 0 | 1 |
| setMean(double[]) | | 100% | | 100% | 0 | 2 | 0 | 4 | 0 | 1 |
| propagate(MultivariateNormalDist.JacobianEvaluator, double[], Matrix) | | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
| propagate(MultivariateNormalDist.JacobianEvaluator, MultivariateNormalDist) | | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
| propagateThisDistribution(MultivariateNormalDist.JacobianEvaluator) | | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
| MultivariateNormalDist(double[], Matrix, boolean) | | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
| MultivariateNormalDist(double[], Matrix) | | 100% | | n/a | 0 | 1 | 0 | 3 | 0 | 1 |
| getCovariance() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| setMeanAndCovariance(double[], Matrix) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| invcdf(double[], double[]) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| invcdf(double[]) | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| invcdf(double, double[]) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| invcdf(double) | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| getCovariance(Matrix) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| setCovariance(Matrix) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| cdf(double[]) | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| mahalanobisDistance(double[]) | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| propagateThisDistribution(MultivariateNormalDist.JacobianEvaluator, MultivariateNormalDist) | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| MultivariateNormalDist() | | 100% | | n/a | 0 | 1 | 0 | 2 | 0 | 1 |
| getMean() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| getCovarianceBasis() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |
| getVariances() | | 100% | | n/a | 0 | 1 | 0 | 1 | 0 | 1 |