Enhance documentation with additional compute examples and stats functions

docs/src/compute.md

@@ -22,8 +22,6 @@ assert_eq!(col_means.data(), &[1.5, 3.5]);
 
 ## Correlation
 
-Correlation functions help measure linear relationships between datasets.
-
 ```rust
 # extern crate rustframe;
 use rustframe::compute::stats::{pearson, covariance};
@@ -51,5 +49,19 @@ let pdf = normal_pdf(x, 0.0, 1.0);
 assert_eq!(pdf.data().len(), 2);
 ```
 
+### More Compute Examples
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+use rustframe::compute::stats::inferential::t_test;
+
+let sample1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
+let sample2 = Matrix::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
+let (t_statistic, p_value) = t_test(&sample1, &sample2);
+assert!((t_statistic + 5.0).abs() < 1e-5);
+assert!(p_value > 0.0 && p_value < 1.0);
+```
+
 With the basics covered, explore predictive models in the
 [machine learning](./machine-learning.md) chapter.

docs/src/data-manipulation.md

@@ -73,5 +73,27 @@ assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
 assert_eq!(frame.sum_horizontal(), vec![4.0, 6.0]);
 ```
 
+## Matrix Operations
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+
+let data1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let data2 = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
+
+let sum = data1.clone() + data2.clone();
+assert_eq!(sum.data(), vec![6.0, 8.0, 10.0, 12.0]);
+
+let product = data1.clone() * data2.clone();
+assert_eq!(product.data(), vec![5.0, 12.0, 21.0, 32.0]);
+
+let scalar_product = data1.clone() * 2.0;
+assert_eq!(scalar_product.data(), vec![2.0, 4.0, 6.0, 8.0]);
+
+let equals = data1 == data1.clone();
+assert_eq!(equals, true);
+```
+
 With the basics covered, continue to the [compute features](./compute.md)
 chapter for statistics and analytics.

docs/src/utilities.md

@@ -35,6 +35,25 @@ let v2 = rng.next_u64();
 assert_ne!(v1, v2);
 ```
 
+## Stats Functions
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+use rustframe::compute::stats::descriptive::{mean, median, stddev};
+
+let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
+
+let mean_value = mean(&data);
+assert_eq!(mean_value, 3.0);
+
+let median_value = median(&data);
+assert_eq!(median_value, 3.0);
+
+let std_value = stddev(&data);
+assert_eq!(std_value, 2.0_f64.sqrt());
+```
+
 Upcoming utilities will cover:
 
 - Data import/export helpers