diff --git a/README.md b/README.md
index 0cc79ed..fd6123c 100644
--- a/README.md
+++ b/README.md
@@ -152,7 +152,7 @@ let zipped_matrix = a.zip(&b, |x, y| x + y);
 assert_eq!(zipped_matrix.data(), &[6.0, 8.0, 10.0, 12.0]);
 ```
 
-### More examples
+## More examples
 
 See the [examples](./examples/) directory for some demonstrations of Rustframe's syntax and functionality.
 
@@ -191,7 +191,7 @@ cargo run --example
 
 Each demo runs a couple of mini-scenarios showcasing the APIs.
 
-### Running benchmarks
+## Running benchmarks
 
 To run the benchmarks, use:
 
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/models/activations.rs b/src/compute/models/activations.rs
index 5391a2b..85ca799 100644
--- a/src/compute/models/activations.rs
+++ b/src/compute/models/activations.rs
@@ -1,3 +1,15 @@
+//! Common activation functions used in neural networks.
+//!
+//! Functions operate element-wise on [`Matrix`] values.
+//!
+//! ```
+//! use rustframe::compute::models::activations::sigmoid;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![0.0], 1, 1);
+//! let y = sigmoid(&x);
+//! assert!((y.get(0,0) - 0.5).abs() < 1e-6);
+//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 pub fn sigmoid(x: &Matrix<f64>) -> Matrix<f64> {
diff --git a/src/compute/models/dense_nn.rs b/src/compute/models/dense_nn.rs
index ab5c05c..bbfae71 100644
--- a/src/compute/models/dense_nn.rs
+++ b/src/compute/models/dense_nn.rs
@@ -1,3 +1,30 @@
+//! A minimal dense neural network implementation for educational purposes.
+//!
+//! Layers operate on [`Matrix`] values and support ReLU and Sigmoid
+//! activations. This is not meant to be a performant deep‑learning framework
+//! but rather a small example of how the surrounding matrix utilities can be
+//! composed.
+//!
+//! ```
+//! use rustframe::compute::models::dense_nn::{ActivationKind, DenseNN, DenseNNConfig, InitializerKind, LossKind};
+//! use rustframe::matrix::Matrix;
+//!
+//! // Tiny network with one input and one output neuron.
+//! let config = DenseNNConfig {
+//!     input_size: 1,
+//!     hidden_layers: vec![],
+//!     output_size: 1,
+//!     activations: vec![ActivationKind::Relu],
+//!     initializer: InitializerKind::Uniform(0.5),
+//!     loss: LossKind::MSE,
+//!     learning_rate: 0.1,
+//!     epochs: 1,
+//! };
+//! let mut nn = DenseNN::new(config);
+//! let x = Matrix::from_vec(vec![1.0, 2.0], 2, 1);
+//! let y = Matrix::from_vec(vec![2.0, 3.0], 2, 1);
+//! nn.train(&x, &y);
+//! ```
 use crate::compute::models::activations::{drelu, relu, sigmoid};
 use crate::matrix::{Matrix, SeriesOps};
 use crate::random::prelude::*;
diff --git a/src/compute/models/gaussian_nb.rs b/src/compute/models/gaussian_nb.rs
index 1e8f38c..d407a23 100644
--- a/src/compute/models/gaussian_nb.rs
+++ b/src/compute/models/gaussian_nb.rs
@@ -1,3 +1,16 @@
+//! Gaussian Naive Bayes classifier for dense matrices.
+//!
+//! ```
+//! use rustframe::compute::models::gaussian_nb::GaussianNB;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![1.0, 2.0, 1.0, 2.0], 2, 2); // two samples
+//! let y = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
+//! let mut model = GaussianNB::new(1e-9, false);
+//! model.fit(&x, &y);
+//! let preds = model.predict(&x);
+//! assert_eq!(preds.rows(), 2);
+//! ```
 use crate::matrix::Matrix;
 use std::collections::HashMap;
 
diff --git a/src/compute/models/k_means.rs b/src/compute/models/k_means.rs
index 05e8c6d..42fa1c4 100644
--- a/src/compute/models/k_means.rs
+++ b/src/compute/models/k_means.rs
@@ -1,3 +1,14 @@
+//! Simple k-means clustering working on [`Matrix`] data.
+//!
+//! ```
+//! use rustframe::compute::models::k_means::KMeans;
+//! use rustframe::matrix::Matrix;
+//!
+//! let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
+//! let (model, labels) = KMeans::fit(&data, 2, 10, 1e-4);
+//! assert_eq!(model.centroids.rows(), 2);
+//! assert_eq!(labels.len(), 2);
+//! ```
 use crate::compute::stats::mean_vertical;
 use crate::matrix::Matrix;
 use crate::random::prelude::*;
diff --git a/src/compute/models/linreg.rs b/src/compute/models/linreg.rs
index ba76197..c5bf083 100644
--- a/src/compute/models/linreg.rs
+++ b/src/compute/models/linreg.rs
@@ -1,3 +1,16 @@
+//! Ordinary least squares linear regression.
+//!
+//! ```
+//! use rustframe::compute::models::linreg::LinReg;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+//! let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
+//! let mut model = LinReg::new(1);
+//! model.fit(&x, &y, 0.01, 100);
+//! let preds = model.predict(&x);
+//! assert_eq!(preds.rows(), 4);
+//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 pub struct LinReg {
diff --git a/src/compute/models/logreg.rs b/src/compute/models/logreg.rs
index ac224aa..56f11b0 100644
--- a/src/compute/models/logreg.rs
+++ b/src/compute/models/logreg.rs
@@ -1,3 +1,16 @@
+//! Binary logistic regression classifier.
+//!
+//! ```
+//! use rustframe::compute::models::logreg::LogReg;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+//! let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
+//! let mut model = LogReg::new(1);
+//! model.fit(&x, &y, 0.1, 100);
+//! let preds = model.predict(&x);
+//! assert_eq!(preds[(0,0)], 0.0);
+//! ```
 use crate::compute::models::activations::sigmoid;
 use crate::matrix::{Matrix, SeriesOps};
 
diff --git a/src/compute/models/mod.rs b/src/compute/models/mod.rs
index 560b9f2..0b95b99 100644
--- a/src/compute/models/mod.rs
+++ b/src/compute/models/mod.rs
@@ -1,3 +1,19 @@
+//! Lightweight machine‑learning models built on matrices.
+//!
+//! Models are intentionally minimal and operate on the [`Matrix`](crate::matrix::Matrix) type for
+//! inputs and parameters.
+//!
+//! ```
+//! use rustframe::compute::models::linreg::LinReg;
+//! use rustframe::matrix::Matrix;
+//!
+//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+//! let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
+//! let mut model = LinReg::new(1);
+//! model.fit(&x, &y, 0.01, 1000);
+//! let preds = model.predict(&x);
+//! assert_eq!(preds.rows(), 4);
+//! ```
 pub mod activations;
 pub mod dense_nn;
 pub mod gaussian_nb;
diff --git a/src/compute/models/pca.rs b/src/compute/models/pca.rs
index a517bd7..f3ec7bd 100644
--- a/src/compute/models/pca.rs
+++ b/src/compute/models/pca.rs
@@ -1,3 +1,14 @@
+//! Principal Component Analysis using covariance matrices.
+//!
+//! ```
+//! use rustframe::compute::models::pca::PCA;
+//! use rustframe::matrix::Matrix;
+//!
+//! let data = Matrix::from_rows_vec(vec![1.0, 1.0, 2.0, 2.0], 2, 2);
+//! let pca = PCA::fit(&data, 1, 0);
+//! let projected = pca.transform(&data);
+//! assert_eq!(projected.cols(), 1);
+//! ```
 use crate::compute::stats::correlation::covariance_matrix;
 use crate::compute::stats::descriptive::mean_vertical;
 use crate::matrix::{Axis, Matrix, SeriesOps};
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;
diff --git a/src/frame/base.rs b/src/frame/base.rs
index 97552cc..cee61bb 100644
--- a/src/frame/base.rs
+++ b/src/frame/base.rs
@@ -1,3 +1,19 @@
+//! Core data-frame structures such as [`Frame`] and [`RowIndex`].
+//!
+//! The [`Frame`] type stores column-labelled data with an optional row index
+//! and builds upon the [`crate::matrix::Matrix`] type.
+//!
+//! # Examples
+//!
+//! ```
+//! use rustframe::frame::{Frame, RowIndex};
+//! use rustframe::matrix::Matrix;
+//!
+//! let data = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
+//! let frame = Frame::new(data, vec!["L", "R"], Some(RowIndex::Int(vec![10, 20])));
+//! assert_eq!(frame.columns(), &["L", "R"]);
+//! assert_eq!(frame.index(), &RowIndex::Int(vec![10, 20]));
+//! ```
 use crate::matrix::Matrix;
 use chrono::NaiveDate;
 use std::collections::HashMap;
diff --git a/src/frame/mod.rs b/src/frame/mod.rs
index ced467c..e5e94b0 100644
--- a/src/frame/mod.rs
+++ b/src/frame/mod.rs
@@ -1,3 +1,21 @@
+//! High-level interface for working with columnar data and row indices.
+//!
+//! The [`Frame`](crate::frame::Frame) type combines a matrix with column labels and a typed row
+//! index, similar to data frames in other data-analysis libraries.
+//!
+//! # Examples
+//!
+//! ```
+//! use rustframe::frame::{Frame, RowIndex};
+//! use rustframe::matrix::Matrix;
+//!
+//! // Build a frame from two columns labelled "A" and "B".
+//! let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+//! let frame = Frame::new(data, vec!["A", "B"], None);
+//!
+//! assert_eq!(frame["A"], vec![1.0, 2.0]);
+//! assert_eq!(frame.index(), &RowIndex::Range(0..2));
+//! ```
 pub mod base;
 pub mod ops;
 
diff --git a/src/frame/ops.rs b/src/frame/ops.rs
index 1c3254b..6030ce1 100644
--- a/src/frame/ops.rs
+++ b/src/frame/ops.rs
@@ -1,3 +1,16 @@
+//! Trait implementations that allow [`Frame`] to reuse matrix operations.
+//!
+//! These modules forward numeric and boolean aggregation methods from the
+//! underlying [`Matrix`](crate::matrix::Matrix) type so that they can be called
+//! directly on a [`Frame`].
+//!
+//! ```
+//! use rustframe::frame::Frame;
+//! use rustframe::matrix::{Matrix, SeriesOps};
+//!
+//! let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0]]), vec!["A"], None);
+//! assert_eq!(frame.sum_vertical(), vec![3.0]);
+//! ```
 use crate::frame::Frame;
 use crate::matrix::{Axis, BoolMatrix, BoolOps, FloatMatrix, SeriesOps};
 
diff --git a/src/matrix/boolops.rs b/src/matrix/boolops.rs
index c830139..bba5e6c 100644
--- a/src/matrix/boolops.rs
+++ b/src/matrix/boolops.rs
@@ -1,3 +1,14 @@
+//! Logical reductions for boolean matrices.
+//!
+//! The [`BoolOps`] trait mirrors common boolean aggregations such as `any` and
+//! `all` over rows or columns of a [`BoolMatrix`].
+//!
+//! ```
+//! use rustframe::matrix::{BoolMatrix, BoolOps};
+//!
+//! let m = BoolMatrix::from_vec(vec![true, false], 2, 1);
+//! assert!(m.any());
+//! ```
 use crate::matrix::{Axis, BoolMatrix};
 
 /// Boolean operations on `Matrix<bool>`
diff --git a/src/matrix/mod.rs b/src/matrix/mod.rs
index 509d449..1b4c372 100644
--- a/src/matrix/mod.rs
+++ b/src/matrix/mod.rs
@@ -1,3 +1,18 @@
+//! Core matrix types and operations.
+//!
+//! The [`Matrix`](crate::matrix::Matrix) struct provides a simple column‑major 2D array with a
+//! suite of numeric helpers. Additional traits like [`SeriesOps`](crate::matrix::SeriesOps) and
+//! [`BoolOps`](crate::matrix::BoolOps) extend functionality for common statistics and logical reductions.
+//!
+//! # Examples
+//!
+//! ```
+//! use rustframe::matrix::Matrix;
+//!
+//! let m = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
+//! assert_eq!(m.shape(), (2, 2));
+//! assert_eq!(m[(0,1)], 3);
+//! ```
 pub mod boolops;
 pub mod mat;
 pub mod seriesops;
diff --git a/src/matrix/seriesops.rs b/src/matrix/seriesops.rs
index 99f89d3..ad6dffc 100644
--- a/src/matrix/seriesops.rs
+++ b/src/matrix/seriesops.rs
@@ -1,3 +1,14 @@
+//! Numeric reductions and transformations over matrix axes.
+//!
+//! [`SeriesOps`] provides methods like [`SeriesOps::sum_vertical`] or
+//! [`SeriesOps::map`] that operate on [`FloatMatrix`] values.
+//!
+//! ```
+//! use rustframe::matrix::{Matrix, SeriesOps};
+//!
+//! let m = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+//! assert_eq!(m.sum_horizontal(), vec![4.0, 6.0]);
+//! ```
 use crate::matrix::{Axis, BoolMatrix, FloatMatrix};
 
 /// "Series-like" helpers that work along a single axis.
diff --git a/src/random/crypto.rs b/src/random/crypto.rs
index 678493f..4f0d694 100644
--- a/src/random/crypto.rs
+++ b/src/random/crypto.rs
@@ -1,3 +1,13 @@
+//! Cryptographically secure random number generator.
+//!
+//! On Unix systems this reads from `/dev/urandom`; on Windows it uses the
+//! system's preferred CNG provider.
+//!
+//! ```
+//! use rustframe::random::{crypto_rng, Rng};
+//! let mut rng = crypto_rng();
+//! let _v = rng.next_u64();
+//! ```
 #[cfg(unix)]
 use std::{fs::File, io::Read};
 
diff --git a/src/random/mod.rs b/src/random/mod.rs
index b40a343..2d0951c 100644
--- a/src/random/mod.rs
+++ b/src/random/mod.rs
@@ -1,3 +1,18 @@
+//! Random number generation utilities.
+//!
+//! Provides both a simple pseudo-random generator [`Prng`](crate::random::Prng) and a
+//! cryptographically secure alternative [`CryptoRng`](crate::random::CryptoRng). The
+//! [`SliceRandom`](crate::random::SliceRandom) trait offers shuffling of slices using any RNG
+//! implementing [`Rng`](crate::random::Rng).
+//!
+//! ```
+//! use rustframe::random::{rng, SliceRandom};
+//!
+//! let mut rng = rng();
+//! let mut data = [1, 2, 3, 4];
+//! data.shuffle(&mut rng);
+//! assert_eq!(data.len(), 4);
+//! ```
 pub mod crypto;
 pub mod prng;
 pub mod random_core;
diff --git a/src/random/prng.rs b/src/random/prng.rs
index 067d163..5d82080 100644
--- a/src/random/prng.rs
+++ b/src/random/prng.rs
@@ -1,3 +1,11 @@
+//! A tiny XorShift64-based pseudo random number generator.
+//!
+//! ```
+//! use rustframe::random::{rng, Rng};
+//! let mut rng = rng();
+//! let x = rng.next_u64();
+//! assert!(x >= 0);
+//! ```
 use std::time::{SystemTime, UNIX_EPOCH};
 
 use crate::random::Rng;
diff --git a/src/random/random_core.rs b/src/random/random_core.rs
index 5db5617..37a5fb9 100644
--- a/src/random/random_core.rs
+++ b/src/random/random_core.rs
@@ -1,3 +1,11 @@
+//! Core traits for random number generators and sampling ranges.
+//!
+//! ```
+//! use rustframe::random::{rng, Rng};
+//! let mut r = rng();
+//! let value: f64 = r.random_range(0.0..1.0);
+//! assert!(value >= 0.0 && value < 1.0);
+//! ```
 use std::f64::consts::PI;
 use std::ops::Range;
 
diff --git a/src/random/seq.rs b/src/random/seq.rs
index 8df863f..760450e 100644
--- a/src/random/seq.rs
+++ b/src/random/seq.rs
@@ -1,3 +1,11 @@
+//! Extensions for shuffling slices with a random number generator.
+//!
+//! ```
+//! use rustframe::random::{rng, SliceRandom};
+//! let mut data = [1, 2, 3];
+//! data.shuffle(&mut rng());
+//! assert_eq!(data.len(), 3);
+//! ```
 use crate::random::Rng;
 
 /// Trait for randomizing slices.
diff --git a/src/utils/dateutils/dates.rs b/src/utils/dateutils/dates.rs
index 2fa6f7f..a5b3481 100644
--- a/src/utils/dateutils/dates.rs
+++ b/src/utils/dateutils/dates.rs
@@ -1,3 +1,10 @@
+//! Generation and manipulation of calendar date sequences.
+//!
+//! ```
+//! use rustframe::utils::dateutils::dates::{DateFreq, DatesList};
+//! let list = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
+//! assert_eq!(list.count().unwrap(), 3);
+//! ```
 use chrono::{Datelike, Duration, NaiveDate, Weekday};
 use std::collections::HashMap;
 use std::error::Error;
diff --git a/src/utils/dateutils/mod.rs b/src/utils/dateutils/mod.rs
index 53ea47c..237d8e0 100644
--- a/src/utils/dateutils/mod.rs
+++ b/src/utils/dateutils/mod.rs
@@ -1,3 +1,13 @@
+//! Generators for sequences of calendar and business dates.
+//!
+//! See [`dates`] for all-day calendars and [`bdates`] for business-day aware
+//! variants.
+//!
+//! ```
+//! use rustframe::utils::dateutils::{DatesList, DateFreq};
+//! let list = DatesList::new("2024-01-01".into(), "2024-01-02".into(), DateFreq::Daily);
+//! assert_eq!(list.count().unwrap(), 2);
+//! ```
 pub mod bdates;
 pub mod dates;
 
diff --git a/src/utils/mod.rs b/src/utils/mod.rs
index e00de87..ef448cc 100644
--- a/src/utils/mod.rs
+++ b/src/utils/mod.rs
@@ -1,3 +1,14 @@
+//! Assorted helper utilities.
+//!
+//! Currently this module exposes date generation utilities in [`dateutils`](crate::utils::dateutils),
+//! including calendar and business date sequences.
+//!
+//! ```
+//! use rustframe::utils::DatesList;
+//! use rustframe::utils::DateFreq;
+//! let dates = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
+//! assert_eq!(dates.count().unwrap(), 3);
+//! ```
 pub mod dateutils;
 
 pub use dateutils::{BDateFreq, BDatesGenerator, BDatesList};