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- Function:
*double***gsl_stats_correlation***(const double*`data1`[], const size_t`stride1`, const double`data2`[], const size_t`stride2`, const size_t`n`) This function efficiently computes the Pearson correlation coefficient between the datasets

`data1`and`data2`which must both be of the same length`n`.r = cov(x, y) / (\Hat\sigma_x \Hat\sigma_y) = {1/(n-1) \sum (x_i - \Hat x) (y_i - \Hat y) \over \sqrt{1/(n-1) \sum (x_i - \Hat x)^2} \sqrt{1/(n-1) \sum (y_i - \Hat y)^2} }

- Function:
*double***gsl_stats_spearman***(const double*`data1`[], const size_t`stride1`, const double`data2`[], const size_t`stride2`, const size_t`n`, double`work`[]) This function computes the Spearman rank correlation coefficient between the datasets

`data1`and`data2`which must both be of the same length`n`. Additional workspace of size 2*`n`is required in`work`. The Spearman rank correlation between vectors*x*and*y*is equivalent to the Pearson correlation between the ranked vectors*x_R*and*y_R*, where ranks are defined to be the average of the positions of an element in the ascending order of the values.