docs/book.toml

@@ -1,7 +1,7 @@
 [book]
-title = "Rustframe User Guide"
+title = "RustFrame User Guide"
-authors = ["Palash Tyagi (https://github.com/Magnus167)"]
+author = ["RustFrame Contributors"]
-description = "Guided journey through Rustframe capabilities."
+description = "Guided journey through RustFrame capabilities."
 
 [build]
 build-dir = "book"

docs/build.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env sh
-# Build and test the Rustframe user guide using mdBook.
+# Build and test the RustFrame user guide using mdBook.
 set -e
 
 cd docs

docs/gen.sh

@@ -11,4 +11,4 @@ mdbook test -L ../target/debug/deps "$@"
 mdbook build "$@"
 
 cargo build
-# cargo build --release
+cargo build --release

docs/src/compute.md

@@ -1,54 +1,30 @@
 # Compute Features
 
-The `compute` module hosts numerical routines for exploratory data analysis.
+The `compute` module provides statistical routines like descriptive
-It covers descriptive statistics, correlations, probability distributions and
+statistics and correlation measures.
-some basic inferential tests.
 
 ## Basic Statistics
 
 ```rust
 # extern crate rustframe;
-use rustframe::compute::stats::{mean, mean_vertical, stddev, median};
+use rustframe::compute::stats::{mean, stddev};
 use rustframe::matrix::Matrix;
 
 let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-assert_eq!(mean(&m), 2.5);
+let mean_val = mean(&m);
-assert_eq!(stddev(&m), 1.118033988749895);
+let std_val = stddev(&m);
-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, covariance};
+use rustframe::compute::stats::pearson;
 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

docs/src/data-manipulation.md

@@ -1,8 +1,7 @@
 # Data Manipulation
 
-Rustframe's `Frame` type couples tabular data with
+RustFrame's `Frame` type couples tabular data with
-column labels and a typed row index. Frames expose a familiar API for loading
+column labels and a typed row index.
-data, selecting rows or columns and performing aggregations.
 
 ## Creating a Frame
 
@@ -18,60 +17,27 @@ assert_eq!(frame["A"], vec![1.0, 2.0]);
 
 ## Indexing Rows
 
-Row labels can be integers, dates or a default range. Retrieving a row returns a
-view that lets you inspect values by column name or position.
-
 ```rust
 # extern crate rustframe;
-# extern crate chrono;
-use chrono::NaiveDate;
 use rustframe::frame::{Frame, RowIndex};
 use rustframe::matrix::Matrix;
 
-let d = |y, m, d| NaiveDate::from_ymd_opt(y, m, d).unwrap();
 let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
-let index = RowIndex::Date(vec![d(2024, 1, 1), d(2024, 1, 2)]);
+let index = RowIndex::Int(vec![10, 20]);
-let mut frame = Frame::new(data, vec!["A", "B"], Some(index));
+let frame = Frame::new(data, vec!["A", "B"], Some(index));
-assert_eq!(frame.get_row_date(d(2024, 1, 2))["B"], 4.0);
+assert_eq!(frame.get_row(20)["B"], 4.0);
-
-// mutate by row key
-frame.get_row_date_mut(d(2024, 1, 1)).set_by_index(0, 9.0);
-assert_eq!(frame.get_row_date(d(2024, 1, 1))["A"], 9.0);
-```
-
-## Column operations
-
-Columns can be inserted, renamed, removed or reordered in place.
-
-```rust
-# extern crate rustframe;
-use rustframe::frame::{Frame, RowIndex};
-use rustframe::matrix::Matrix;
-
-let data = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
-let mut frame = Frame::new(data, vec!["X", "Y"], Some(RowIndex::Range(0..2)));
-
-frame.add_column("Z", vec![5, 6]);
-frame.rename("Y", "W");
-let removed = frame.delete_column("X");
-assert_eq!(removed, vec![1, 2]);
-frame.sort_columns();
-assert_eq!(frame.columns(), &["W", "Z"]);
 ```
 
 ## Aggregations
 
-Any numeric aggregation available on `Matrix` is forwarded to `Frame`.
-
 ```rust
 # extern crate rustframe;
 use rustframe::frame::Frame;
 use rustframe::matrix::{Matrix, SeriesOps};
 
-let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]), vec!["A", "B"], None);
+let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0]]), vec!["A"], None);
-assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
+assert_eq!(frame.sum_vertical(), vec![3.0]);
-assert_eq!(frame.sum_horizontal(), vec![4.0, 6.0]);
 ```
 
-With the basics covered, continue to the [compute features](./compute.md)
+When you're ready to run analytics, continue to the
-chapter for statistics and analytics.
+[compute features](./compute.md) chapter.

docs/src/introduction.md

@@ -1,38 +1,10 @@
 # Introduction
 
-Welcome to the **Rustframe User Guide**. Rustframe is a lightweight dataframe
+Welcome to the **RustFrame User Guide**. This book provides a tour of
-and math toolkit for Rust written in 100% safe Rust. It focuses on keeping the
+RustFrame's capabilities from basic data handling to advanced machine learning
-API approachable while offering handy features for small analytical or
+workflows. Each chapter contains runnable snippets so you can follow along.
-educational projects.
 
-Rustframe bundles:
+1. [Data manipulation](./data-manipulation.md) for loading and transforming data.
-
+2. [Compute features](./compute.md) for statistics and analytics.
-- column‑labelled frames built on a fast column‑major matrix
+3. [Machine learning](./machine-learning.md) for predictive models.
-- familiar element‑wise math and aggregation routines
+4. [Utilities](./utilities.md) for supporting helpers and upcoming modules.
-- a growing `compute` module for statistics and machine learning
-- utilities for dates and random numbers
-
-```rust
-# extern crate rustframe;
-use rustframe::{frame::Frame, matrix::{Matrix, SeriesOps}};
-
-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);
-
-// Perform column wise aggregation
-assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
-```
-
-## Resources
-
-- [GitHub repository](https://github.com/Magnus167/rustframe)
-- [Crates.io](https://crates.io/crates/rustframe) & [API docs](https://docs.rs/rustframe)
-- [Code coverage](https://codecov.io/gh/Magnus167/rustframe)
-
-This guide walks through the main building blocks of the library. Each chapter
-contains runnable snippets so you can follow along:
-
-1. [Data manipulation](./data-manipulation.md) for loading and transforming data
-2. [Compute features](./compute.md) for statistics and analytics
-3. [Machine learning](./machine-learning.md) for predictive models
-4. [Utilities](./utilities.md) for supporting helpers and upcoming modules

docs/src/machine-learning.md

@@ -1,17 +1,11 @@
 # Machine Learning
 
-The `compute::models` module bundles several learning algorithms that operate on
+RustFrame ships with several algorithms:
-`Matrix` structures. These examples highlight the basic training and prediction
-APIs. For more end‑to‑end walkthroughs see the examples directory in the
-repository.
-
-Currently implemented models include:
 
 - Linear and logistic regression
-- K‑means clustering
+- K-means clustering
 - Principal component analysis (PCA)
-- Gaussian Naive Bayes
+- Naive Bayes and dense neural networks
-- Dense neural networks
 
 ## Linear Regression
 
@@ -43,34 +37,3 @@ let cluster = model.predict(&new_point)[0];
 
 For helper functions and upcoming modules, visit the
 [utilities](./utilities.md) section.
-
-## Logistic Regression
-
-```rust
-# extern crate rustframe;
-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, 200);
-let preds = model.predict_proba(&x);
-assert_eq!(preds.rows(), 4);
-```
-
-## Principal Component Analysis
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::pca::PCA;
-use rustframe::matrix::Matrix;
-
-let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let pca = PCA::fit(&data, 1, 0);
-let transformed = pca.transform(&data);
-assert_eq!(transformed.cols(), 1);
-```
-
-For helper functions and upcoming modules, visit the
-[utilities](./utilities.md) section.

docs/src/utilities.md

@@ -3,36 +3,16 @@
 Utilities provide handy helpers around the core library. Existing tools
 include:
 
-- Date utilities for generating calendar sequences and business‑day sets
+- Date utilities for generating calendar sequences.
-- Random number generators for simulations and testing
 
 ## Date Helpers
 
 ```rust
 # extern crate rustframe;
-use rustframe::utils::dateutils::{BDatesList, BDateFreq, DatesList, DateFreq};
+use rustframe::utils::dateutils::{DatesList, DateFreq};
 
-// Calendar sequence
 let list = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
 assert_eq!(list.count().unwrap(), 3);
-
-// Business days starting from 2024‑01‑02
-let bdates = BDatesList::from_n_periods("2024-01-02".into(), BDateFreq::Daily, 3).unwrap();
-assert_eq!(bdates.list().unwrap().len(), 3);
-```
-
-## Random Numbers
-
-The `random` module offers deterministic and cryptographically secure RNGs.
-
-```rust
-# extern crate rustframe;
-use rustframe::random::{Prng, Rng};
-
-let mut rng = Prng::new(42);
-let v1 = rng.next_u64();
-let v2 = rng.next_u64();
-assert_ne!(v1, v2);
 ```
 
 Upcoming utilities will cover: