diff --git a/docs/src/compute.md b/docs/src/compute.md
index 33b9d6e..8727935 100644
--- a/docs/src/compute.md
+++ b/docs/src/compute.md
@@ -1,30 +1,54 @@
 # Compute Features
 
-The `compute` module provides statistical routines like descriptive
-statistics and correlation measures.
+The `compute` module hosts numerical routines for exploratory data analysis.
+It covers descriptive statistics, correlations, probability distributions and
+some basic inferential tests.
 
 ## Basic Statistics
 
 ```rust
 # extern crate rustframe;
-use rustframe::compute::stats::{mean, stddev};
+use rustframe::compute::stats::{mean, mean_vertical, stddev, median};
 use rustframe::matrix::Matrix;
 
 let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let mean_val = mean(&m);
-let std_val = stddev(&m);
+assert_eq!(mean(&m), 2.5);
+assert_eq!(stddev(&m), 1.118033988749895);
+assert_eq!(median(&m), 2.5);
+// column averages returned as 1 x n matrix
+let col_means = mean_vertical(&m);
+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;
+use rustframe::compute::stats::{pearson, covariance};
 use rustframe::matrix::Matrix;
 
 let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
 let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
 let corr = pearson(&x, &y);
+let cov = covariance(&x, &y);
+assert!((corr - 1.0).abs() < 1e-8);
+assert!((cov - 2.5).abs() < 1e-8);
+```
+
+## Distributions
+
+Probability distribution helpers are available for common PDFs and CDFs.
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::distributions::normal_pdf;
+use rustframe::matrix::Matrix;
+
+let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
+let pdf = normal_pdf(x, 0.0, 1.0);
+assert_eq!(pdf.data().len(), 2);
 ```
 
 With the basics covered, explore predictive models in the