assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);

    assertThat(manyValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =

        return value;
      }
    }
    return null;
  }

  /**
   * A sensible definition of {@link #clear} in terms of the {@code iterator} method of {@link
   * #entrySet}. In many cases, you may wish to override {@link #clear} to forward to this
   * implementation.
   *
   * @since 7.0
   */
  protected void standardClear() {
    Iterators.clear(entrySet().iterator());
  }

        .of(MANY_VALUES_MEAN);
    assertThat(manyValuesAccumulatorByAddAllStatsAccumulator.mean())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();

        return value;
      }
    }
    return null;
  }

  /**
   * A sensible definition of {@link #clear} in terms of the {@code iterator} method of {@link
   * #entrySet}. In many cases, you may wish to override {@link #clear} to forward to this
   * implementation.
   *
   * @since 7.0
   */
  protected void standardClear() {
    Iterators.clear(entrySet().iterator());
  }

import java.util.Map.Entry;
import java.util.Set;
import java.util.Spliterator;
import java.util.function.BiConsumer;
import org.jspecify.annotations.Nullable;

/**
 * A {@link Multimap} whose contents will never change, with many other important properties
 * detailed at {@link ImmutableCollection}.
 *
 * <p><b>Warning:</b> avoid <i>direct</i> usage of {@link ImmutableMultimap} as a type (as with

 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as
 *       thoroughly as possible.

 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as
 *       thoroughly as possible.