Fix model fitting failure for a standard error equal to zero

This would result in a t-value of Nan, which gets rejected by
students_t_cdf. The previous implementation silently forwarded the NaN.
This fixed version produces a p-value of 1.0 instead.

src/lib.rs

@@ -925,7 +925,10 @@ impl LowLevelRegressionModel {
         let centered_tss = centered_input_matrix.dot(&centered_input_matrix);
         let rsquared = 1. - (ssr / centered_tss);
         let rsquared_adj = 1. - ((n - 1) as f64 / df_resid as f64 * (1. - rsquared));
-        let tvalues = parameters.iter().zip(se.iter()).map(|(x, y)| x / y);
+        let tvalues = parameters
+            .iter()
+            .zip(se.iter())
+            .map(|(&x, &y)| x / y.max(std::f64::EPSILON));
         let pvalues: Vec<f64> = tvalues
             .map(|x| students_t_cdf(x.abs().neg(), df_resid as i64).map(|i| i * 2.))
             .collect::<Option<_>>()

src/tests.rs

@@ -102,6 +102,82 @@ fn test_pinv_with_data_columns() {
     assert_almost_eq!(regression.scale(), scale);
 }
 
+#[test]
+fn test_standard_error_equal_to_zero_does_not_prevent_fitting() {
+    // Regression test for underlying issue of https://github.com/n1m3/linregress/issues/9
+
+    // The following input does not conform to our API (we expect that all intercepts == 1, not 0),
+    // but Hyrum's law...
+    let data = vec![
+        0.0,
+        0.0,
+        0.0,
+        34059798.0,
+        0.0,
+        1.0,
+        66771421.0,
+        0.0,
+        2.0,
+        100206133.0,
+        0.0,
+        3.0,
+        133435943.0,
+        0.0,
+        4.0,
+        166028256.0,
+        0.0,
+        5.0,
+        199723152.0,
+        0.0,
+        6.0,
+        233754352.0,
+        0.0,
+        7.0,
+        267284084.0,
+        0.0,
+        8.0,
+        301756656.0,
+        0.0,
+        9.0,
+        331420366.0,
+        0.0,
+        10.0,
+        367961084.0,
+        0.0,
+        11.0,
+        401288216.0,
+        0.0,
+        12.0,
+        434555574.0,
+        0.0,
+        13.0,
+        469093436.0,
+        0.0,
+        14.0,
+        501541551.0,
+        0.0,
+        15.0,
+        523986797.0,
+        0.0,
+        16.0,
+        558792615.0,
+        0.0,
+        17.0,
+        631494010.0,
+        0.0,
+        18.0,
+        669229109.0,
+        0.0,
+        19.0,
+        704321427.0,
+        0.0,
+        20.0,
+    ];
+    let rows = 21;
+    let columns = 3;
+    fit_low_level_regression_model(&data, rows, columns).unwrap();
+}
+
 #[test]
 fn test_low_level_model_fitting() {
     let inputs = vec![1., 3., 4., 5., 2., 3., 4.];