diff --git a/src/compute/mod.rs b/src/compute/mod.rs
index 6aa9b32..abe5eaf 100644
--- a/src/compute/mod.rs
+++ b/src/compute/mod.rs
@@ -1,3 +1,16 @@
+//! Algorithms and statistical utilities built on top of the core matrices.
+//!
+//! This module groups together machine‑learning models and statistical helper
+//! functions. For quick access to basic statistics see [`stats`](crate::compute::stats), while
+//! [`models`](crate::compute::models) contains small learning algorithms.
+//!
+//! ```
+//! use rustframe::compute::stats;
+//! use rustframe::matrix::Matrix;
+//!
+//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0], 3, 1);
+//! assert_eq!(stats::mean(&m), 2.0);
+//! ```
 pub mod models;
 
 pub mod stats;
diff --git a/src/compute/stats/correlation.rs b/src/compute/stats/correlation.rs
index ac9ee19..3e2aa9f 100644
--- a/src/compute/stats/correlation.rs
+++ b/src/compute/stats/correlation.rs
@@ -1,3 +1,16 @@
+//! Covariance and correlation helpers.
+//!
+//! This module provides routines for measuring the relationship between
+//! columns or rows of matrices.
+//!
+//! ```
+//! use rustframe::compute::stats::correlation;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+//! let cov = correlation::covariance(&x, &x);
+//! assert!((cov - 1.25).abs() < 1e-8);
+//! ```
 use crate::compute::stats::{mean, mean_horizontal, mean_vertical, stddev};
 use crate::matrix::{Axis, Matrix, SeriesOps};
 
diff --git a/src/compute/stats/descriptive.rs b/src/compute/stats/descriptive.rs
index 71510ff..686f195 100644
--- a/src/compute/stats/descriptive.rs
+++ b/src/compute/stats/descriptive.rs
@@ -1,3 +1,15 @@
+//! Descriptive statistics for matrices.
+//!
+//! Provides means, variances, medians and other aggregations computed either
+//! across the whole matrix or along a specific axis.
+//!
+//! ```
+//! use rustframe::compute::stats::descriptive;
+//! use rustframe::matrix::Matrix;
+//!
+//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+//! assert_eq!(descriptive::mean(&m), 2.5);
+//! ```
 use crate::matrix::{Axis, Matrix, SeriesOps};
 
 pub fn mean(x: &Matrix<f64>) -> f64 {
diff --git a/src/compute/stats/distributions.rs b/src/compute/stats/distributions.rs
index 63534d5..25fbdc1 100644
--- a/src/compute/stats/distributions.rs
+++ b/src/compute/stats/distributions.rs
@@ -1,3 +1,16 @@
+//! Probability distribution functions applied element-wise to matrices.
+//!
+//! Includes approximations for the normal, uniform and gamma distributions as
+//! well as the error function.
+//!
+//! ```
+//! use rustframe::compute::stats::distributions;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![0.0], 1, 1);
+//! let pdf = distributions::normal_pdf(x.clone(), 0.0, 1.0);
+//! assert!((pdf.get(0,0) - 0.3989).abs() < 1e-3);
+//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 use std::f64::consts::PI;
diff --git a/src/compute/stats/inferential.rs b/src/compute/stats/inferential.rs
index 8a6f28a..b635c37 100644
--- a/src/compute/stats/inferential.rs
+++ b/src/compute/stats/inferential.rs
@@ -1,3 +1,14 @@
+//! Basic inferential statistics such as t‑tests and chi‑square tests.
+//!
+//! ```
+//! use rustframe::compute::stats::inferential;
+//! use rustframe::matrix::Matrix;
+//!
+//! let a = Matrix::from_vec(vec![1.0, 2.0], 2, 1);
+//! let b = Matrix::from_vec(vec![1.1, 1.9], 2, 1);
+//! let (t, _p) = inferential::t_test(&a, &b);
+//! assert!(t.abs() < 1.0);
+//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 use crate::compute::stats::{gamma_cdf, mean, sample_variance};
diff --git a/src/compute/stats/mod.rs b/src/compute/stats/mod.rs
index ef7e420..405cf6c 100644
--- a/src/compute/stats/mod.rs
+++ b/src/compute/stats/mod.rs
@@ -1,3 +1,16 @@
+//! Statistical routines for matrices.
+//!
+//! Functions are grouped into submodules for descriptive statistics,
+//! correlations, probability distributions and basic inferential tests.
+//!
+//! ```
+//! use rustframe::compute::stats;
+//! use rustframe::matrix::Matrix;
+//!
+//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+//! let cov = stats::covariance(&m, &m);
+//! assert!((cov - 1.25).abs() < 1e-8);
+//! ```
 pub mod correlation;
 pub mod descriptive;
 pub mod distributions;