}

  /** Returns whether this is a vertical transformation. */
  public abstract boolean isVertical();

  /** Returns whether this is a horizontal transformation. */
  public abstract boolean isHorizontal();

  /**
   * Returns the slope of the transformation, i.e. the rate of change of {@code y} with respect to
   * {@code x}. This must not be called on a vertical transformation (i.e. when {@link

   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This

    double x = 1.2;
    LinearTransformation transformation = LinearTransformation.vertical(x);
    assertVerticalLinearTransformation(transformation, x);
  }

  public void testVertical_infiniteX() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.vertical(Double.NEGATIVE_INFINITY));
  }

  public void testVertical_nanX() {

    double x = 1.2;
    LinearTransformation transformation = LinearTransformation.vertical(x);
    assertVerticalLinearTransformation(transformation, x);
  }

  public void testVertical_infiniteX() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.vertical(Double.NEGATIVE_INFINITY));
  }

  public void testVertical_nanX() {

        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());
    assertThrows(IllegalStateException.class, () -> CONSTANT_VALUES_PAIRED_STATS.leastSquaresFit());
  }

  public void testEqualsAndHashCode() {

        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());
    assertThrows(IllegalStateException.class, () -> CONSTANT_VALUES_PAIRED_STATS.leastSquaresFit());
  }

  public void testEqualsAndHashCode() {

    checkArgument(xDelta != 0.0);
    checkArgument(yDelta != 0.0);
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.inverse().isHorizontal()).isFalse();
    assertThat(transformation.inverse().isVertical()).isFalse();
    assertThat(transformation.transform(x1)).isWithin(ALLOWED_ERROR).of(y1);

   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This

    checkArgument(xDelta != 0.0);
    checkArgument(yDelta != 0.0);
    assertThat(transformation.isHorizontal()).isFalse();
    assertThat(transformation.isVertical()).isFalse();
    assertThat(transformation.inverse().isHorizontal()).isFalse();
    assertThat(transformation.inverse().isVertical()).isFalse();
    assertThat(transformation.transform(x1)).isWithin(ALLOWED_ERROR).of(y1);

   */
  public static final byte LF = 10;

  /**
   * Alternate name for {@link #LF}. ({@code LF} is preferred.)
   *
   * @since 8.0
   */
  public static final byte NL = 10;

  /**
   * Vertical Tabulation ('\v'): A format effector which controls the movement of the printing
   * position to the next in a series of predetermined printing lines. (Applicable also to display
   * devices.)
   *
   * @since 8.0