* Los validadores también permiten que se definan fácil y claramente schemas complejos de datos. Estos son chequeados y documentados como JSON Schema.
    * Puedes tener objetos de **JSON profundamente anidados** y que todos sean validados y anotados.
* **Extensible**:

          + (-22.0 - INTEGER_MANY_VALUES_MEAN) * (-22.0 - INTEGER_MANY_VALUES_MEAN)
          + (3333.0 - INTEGER_MANY_VALUES_MEAN) * (3333.0 - INTEGER_MANY_VALUES_MEAN)
          + (-4444.0 - INTEGER_MANY_VALUES_MEAN) * (-4444.0 - INTEGER_MANY_VALUES_MEAN)
          + (555555.0 - INTEGER_MANY_VALUES_MEAN) * (555555.0 - INTEGER_MANY_VALUES_MEAN);
  static final double INTEGER_MANY_VALUES_MAX = 555555.0;

   */
  @Deprecated
  public static double mean(long... values) {
    checkArgument(values.length > 0, "Cannot take mean of 0 values");
    long count = 1;
    double mean = values[0];
    for (int index = 1; index < values.length; ++index) {
      count++;
      // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15)
      mean += (values[index] - mean) / count;
    }
    return mean;
  }

  /**

      DoubleMath.mean(Double.POSITIVE_INFINITY);
      fail("Expected IllegalArgumentException");
    } catch (IllegalArgumentException expected) {
    }
  }

  @GwtIncompatible // DoubleMath.mean
  public void testMean_intVarargs() {
    assertEquals(-13.75, DoubleMath.mean(11, -22, 44, -88), 1.0e-10);
    assertEquals(11.0, DoubleMath.mean(11), 1.0e-10);
  }

  @GwtIncompatible // DoubleMath.mean

      }
    }
    assertThat(MANY_VALUES_STATS_ITERABLE.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);
    assertThat(MANY_VALUES_STATS_ITERATOR.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);
    assertThat(MANY_VALUES_STATS_SNAPSHOT.mean()).isWithin(ALLOWED_ERROR).of(MANY_VALUES_MEAN);
    assertThat(LARGE_VALUES_STATS.mean())
        .isWithin(ALLOWED_ERROR * Double.MAX_VALUE)
        .of(LARGE_VALUES_MEAN);

          + (-22.0 - INTEGER_MANY_VALUES_MEAN) * (-22.0 - INTEGER_MANY_VALUES_MEAN)
          + (3333.0 - INTEGER_MANY_VALUES_MEAN) * (3333.0 - INTEGER_MANY_VALUES_MEAN)
          + (-4444.0 - INTEGER_MANY_VALUES_MEAN) * (-4444.0 - INTEGER_MANY_VALUES_MEAN)
          + (555555.0 - INTEGER_MANY_VALUES_MEAN) * (555555.0 - INTEGER_MANY_VALUES_MEAN);
  static final double INTEGER_MANY_VALUES_MAX = 555555.0;

    assertThat(twoValuesAccumulatorByAddAllStats.mean())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllIterable.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllIterator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);

      }
    } else {
      count++;
      if (isFinite(value) && isFinite(mean)) {
        // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15) and (16)
        double delta = value - mean;
        mean += delta / count;
        sumOfSquaresOfDeltas += delta * (value - mean);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
        sumOfSquaresOfDeltas = NaN;
      }
      min = Math.min(min, value);

      // in NaN naturally.
      sumOfProductsOfDeltas +=
          values.sumOfProductsOfDeltas()
              + (values.xStats().mean() - xStats.mean())
                  * (values.yStats().mean() - yStats.mean())
                  * values.count();
    }
    yStats.addAll(values.yStats());
  }

  /** Returns an immutable snapshot of the current statistics. */

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