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() {

}
table.overviewSummary td.colFirst, table.overviewSummary th.colFirst,
table.overviewSummary td.colOne, table.overviewSummary th.colOne {
    width:25%;
    vertical-align:middle;
}
table.packageSummary td.colFirst, table.overviewSummary th.colFirst {
    width:25%;
    vertical-align:middle;
}
/*
Content styles
*/
.description pre {
    margin-top:0;
}
.deprecatedContent {
    margin:0;

-item-text{display:block;padding:.25rem 1.5rem;color:#212529}.btn-group,.btn-group-vertical{position:relative;display:-ms-inline-flexbox;display:inline-flex;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;-ms-flex:1 1 auto;flex:1 1 auto}.btn-group-vertical>.btn:hover,.btn-group>.btn:hover{z-index:1}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus{z-in...

   * 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

-item-text{display:block;padding:.25rem 1.5rem;color:#212529}.btn-group,.btn-group-vertical{position:relative;display:-ms-inline-flexbox;display:inline-flex;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;-ms-flex:1 1 auto;flex:1 1 auto}.btn-group-vertical>.btn:hover,.btn-group>.btn:hover{z-index:1}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus{z-in...

   * 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

  transform: rotate(135deg);
  -webkit-transform: rotate(135deg);
}

.daterangepicker .calendar-table th, .daterangepicker .calendar-table td {
  white-space: nowrap;
  text-align: center;
  vertical-align: middle;
  min-width: 32px;
  width: 32px;
  height: 24px;
  line-height: 24px;
  font-size: 12px;
  border-radius: 4px;
  border: 1px solid transparent;
  white-space: nowrap;

0.75rem;\n  padding-left: 0.75rem;\n}\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n}\n\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group {\n  width: 100%;\n}\n\n.btn-group-vertical > .btn:not(:first-child),\n.btn-group-vertical > .btn-group:not(:first-child) {\n  margin-top: -1px;\n}\n\n.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle),\n.btn-group-vertical > .btn-group:not(:last-child) > .btn {\n  border-...

   * 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