docs(stats_test): better attribution of sources

src/stats_tests/chisquare.rs

@@ -40,12 +40,13 @@ impl std::error::Error for ChiSquareTestError {}
 ///
 /// # Remarks
 ///
-/// Implementation based on the one-way chi-square test of [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html#scipy.stats.chisquare).
-/// and Pearson's chi-squared test [wikipedia] article.
-///
 /// `ddof` represents an adjustment that can be made to the degrees of freedom where the unadjusted
 /// degrees of freedom is `f_obs.len() - 1`.
 ///
+/// Implementation based on [wikipedia](https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test)
+/// while aligning to [scipy's](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html)
+/// function header where possible. The scipy implementation was also used for testing and validation.
+///
 /// # Examples
 ///
 /// ```

src/stats_tests/f_oneway.rs

@@ -46,18 +46,21 @@ impl std::error::Error for FOneWayTestError {}
 /// Takes in a set (outer vector) of samples (inner vector) and returns the F-statistic and p-value
 ///
 /// # Remarks
-/// Implementation based on [statsdirect](https://www.statsdirect.com/help/analysis_of_variance/one_way.htm)
-/// and [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html#scipy.stats.f_oneway)
 ///
 /// `samples` needs to be mutable in case needing to filter out NaNs for NaNPolicy::Emit
 ///
+/// Implementation based on [statsdirect](https://www.statsdirect.com/help/analysis_of_variance/one_way.htm)
+/// while aligning to [scipy's](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html#scipy.stats.f_oneway)
+/// function header where possible. The scipy implementation was also used for testing and
+/// validation. Includes the use of [McDonald et al. (1991)](doi.org/10.1007/BF01319403) for
+/// testing and validation.
+///
 /// # Examples
 ///
 /// ```
 /// use statrs::stats_tests::f_oneway::f_oneway;
 /// use statrs::stats_tests::NaNPolicy;
 ///
-/// // based on wikipedia example
 /// let a1 = Vec::from([6f64, 8f64, 4f64, 5f64, 3f64, 4f64]);
 /// let a2 = Vec::from([8f64, 12f64, 9f64, 11f64, 6f64, 8f64]);
 /// let a3 = Vec::from([13f64, 9f64, 11f64, 8f64, 7f64, 12f64]);
@@ -149,8 +152,6 @@ mod tests {
 
     #[test]
     fn test_scipy_example() {
-        // Test against the scipy example
-        // https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.f_oneway.html#scipy.stats.f_oneway
         let tillamook = Vec::from([
             0.0571, 0.0813, 0.0831, 0.0976, 0.0817, 0.0859, 0.0735, 0.0659, 0.0923, 0.0836,
         ]);

src/stats_tests/mannwhitneyu.rs

@@ -213,10 +213,17 @@ fn calc_mwu_exact_pvalue(u: f64, n1: usize, n2: usize) -> f64 {
 /// samples sizes (length of `x` + length of `y`) above 20 are approximated fairly well using the
 /// asymptotic (normal) methods.
 ///
-/// Implementation was largely based on the [scipy version](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html#scipy.stats.mannwhitneyu).
-/// There are a few deviations including, not supporting calculation of the value via permutation
-/// tests, not supporting calculation of the exact p-value where input data includes ties, and not
-/// supporting the NaN policy due to being generic on T which might not have NaN values.
+///
+/// Implementation based on [wikipedia](https://en.wikipedia.org/wiki/Mann–Whitney_U_test)
+/// while aligning to [scipy's](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html#scipy.stats.mannwhitneyu)
+/// function header where possible. The scipy implementation was also used for testing and
+/// validation. Includes the use of [Shier (2004)](https://www.statstutor.ac.uk/resources/uploaded/mannwhitney.pdf) for
+/// testing and validation.
+///
+/// There are a few deviations from the scipy version including, not supporting calculation
+/// of the value via permutation tests, not supporting calculation of the exact p-value
+/// where input data includes ties, and not supporting the NaN policy due to being generic
+/// on T which might not have NaN values.
 ///
 /// # Examples
 ///

src/stats_tests/skewtest.rs

@@ -51,12 +51,15 @@ fn calc_root_b1(data: &[f64]) -> f64 {
 ///
 /// # Remarks
 ///
-/// Implementation based on [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skewtest.html#scipy.stats.skewtest).
-/// and [fintools.com](https://www.fintools.com/docs/normality_correlation.pdf) which both
-/// reference D'Agostino, 1970 (but direct access to the paper has been challenging to find)
-///
 /// `a` needs to be mutable in case needing to filter out NaNs for NaNPolicy::Emit
 ///
+/// Implementation based on [fintools.com](https://www.fintools.com/docs/normality_correlation.pdf)
+/// which indirectly uses [D'Agostino, (1970)](https://doi.org/10.2307/2684359)
+/// while aligning to [scipy's](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skewtest.html#scipy.stats.skewtest)
+/// function header where possible. The scipy implementation was also used for testing and validation.
+/// Includes the use of [Shapiro & Wilk (1965)](https://doi.org/10.2307/2333709) for
+/// testing and validation.
+///
 /// # Examples
 ///
 /// ```

src/stats_tests/ttest_onesample.rs

@@ -37,10 +37,12 @@ impl std::error::Error for TTestOneSampleError {}
 ///
 /// # Remarks
 ///
-/// Implementation based on [scipy](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_1samp.html).
-///
 /// `a` needs to be mutable in case needing to filter out NaNs for NaNPolicy::Emit
 ///
+/// Implementation based on [jmp](https://www.jmp.com/en_us/statistics-knowledge-portal/t-test/one-sample-t-test.html)
+/// while aligning to [scipy's](https://docs.scipy.org/doc/scipy-1.14.1/reference/generated/scipy.stats.ttest_1samp.html)
+/// function header where possible.
+///
 /// # Examples
 ///
 /// ```