assertThat(verticalValuesAccumulatorByAddAllPartitionedPairedStats.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(0.0);
    assertThat(constantValuesAccumulator.sampleCovariance()).isWithin(ALLOWED_ERROR).of(0.0);
    assertThat(constantValuesAccumulatorByAddAllPartitionedPairedStats.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(0.0);
  }

    assertThat(oneValueAccumulator.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(oneValueAccumulatorByAddAllEmptyStats.mean()).isWithin(ALLOWED_ERROR).of(ONE_VALUE);
    assertThat(twoValuesAccumulator.mean()).isWithin(ALLOWED_ERROR).of(TWO_VALUES_MEAN);
    assertThat(twoValuesAccumulatorByAddAllStats.mean())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_MEAN);

    assertThat(DoubleMath.mean(11, -22, 44, -88)).isWithin(1.0e-10).of(-13.75);
    assertThat(DoubleMath.mean(11)).isWithin(1.0e-10).of(11.0);
  }

  @GwtIncompatible // DoubleMath.mean
  @SuppressWarnings("deprecation") // test of deprecated method
  public void testMean_longVarargs() {
    assertThat(DoubleMath.mean(11L, -22L, 44L, -88L)).isWithin(1.0e-10).of(-13.75);

    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

    for (QuantilesAlgorithm algorithm : NON_REFERENCE_ALGORITHMS) {
      assertWithMessage("Mismatch between %s and %s", algorithm, REFERENCE_ALGORITHM)
          .that(algorithm.singleQuantile(1, 2, dataset.clone()))
          .isWithin(ALLOWED_ERROR)
          .of(referenceValue);
    }
  }

  public void testSingleQuantile_percentile99() {
    double referenceValue = REFERENCE_ALGORITHM.singleQuantile(99, 100, dataset.clone());

    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    stopwatch.sleepMillis(400);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.2);
  }

  public void testOneSecondBurst() {

    RateLimiter limiter = RateLimiter.create(5.0, stopwatch);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    stopwatch.sleepMillis(400);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.0);
    assertThat(limiter.acquire()).isWithin(EPSILON).of(0.2);
  }

  public void testOneSecondBurst() {

    assertEquals(knownNumberOfFalsePositives, numFpp);
    double expectedReportedFpp = (double) knownNumberOfFalsePositives / numInsertions;
    double actualReportedFpp = bf.expectedFpp();
    assertThat(actualReportedFpp).isWithin(0.00015).of(expectedReportedFpp);
  }

  public void testCreateAndCheckBloomFilterWithKnownFalsePositives64() {
    int numInsertions = 1000000;
    BloomFilter<String> bf =
        BloomFilter.create(

    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.slope()).isWithin(ALLOWED_ERROR).of(slope);
    // Note that we cannot test the actual mapping of points, as the results will be unreliable due
    // to loss of precision with this value of the slope.
  }