Overview

statcpp is a header-only statistics library written in C++17. It provides a wide range of statistical functionality, from basic statistics to advanced hypothesis testing and regression analysis.

Key Features

Header-Only: No build required, just include and use
C++17 Standard Compliant: Leverages modern C++ features
STL Style: Intuitive random access iterator-based API
Projection Support: Directly process struct members, etc.
Comprehensive Testing: Extensive test suite with Google Test
Cross-Platform: Tested on macOS and Linux

Provided Functionality

Basic Statistics: Mean, median, mode, variance, standard deviation, etc.
Order Statistics: Quartiles, percentiles, five-number summary
Correlation Analysis: Pearson, Spearman, Kendall correlation coefficients
Probability Distributions: Normal, t, chi-squared, F, binomial, Poisson, etc.
Hypothesis Tests: t-test, z-test, F-test, chi-squared test, Wilcoxon test, Mann-Whitney test, etc.
Effect Sizes: Cohen's d, Hedges' g, eta squared, etc.
Regression Analysis: Simple, multiple, logistic regression
ANOVA: One-way, two-way ANOVA
Resampling: Bootstrap, permutation test
Power Analysis: Sample size calculation and power analysis
Distance Metrics: Euclidean distance, Manhattan distance, cosine similarity, etc.
Clustering: k-means, hierarchical clustering

Quick Start

Installation

# Clone the repository
git clone https://github.com/mitsuruk/statcpp.git
 
# Add header files to include path
# Method 1: Install to system
cd statcpp
mkdir build && cd build
cmake ..
sudo cmake --install .
 
# Method 2: Copy to your project
cp -r statcpp/include/statcpp /your/project/include/

For details, see Installation Guide.

Basic Usage

#include <iostream>
#include <vector>
#include <algorithm>
#include "statcpp/basic_statistics.hpp"
#include "statcpp/dispersion_spread.hpp"
#include "statcpp/order_statistics.hpp"
 
int main() {
    std::vector<double> data = {5.0, 2.0, 8.0, 1.0, 3.0, 7.0, 4.0};
 
    // Basic statistics
    double avg = statcpp::mean(data.begin(), data.end());
    double sd = statcpp::stddev(data.begin(), data.end());
 
    std::cout << "Mean: " << avg << std::endl;              // 4.285...
    std::cout << "Standard Deviation: " << sd << std::endl; // 2.429...
 
    // Order statistics (sorting required)
    std::sort(data.begin(), data.end());
    double median = statcpp::median(data.begin(), data.end());
    auto q = statcpp::quartiles(data.begin(), data.end());
 
    std::cout << "Median: " << median << std::endl;         // 4.0
    std::cout << "First Quartile: " << q.q1 << std::endl;   // 2.5
    std::cout << "Third Quartile: " << q.q3 << std::endl;   // 6.5
 
    return 0;
}

Compile and run:

g++ -std=c++17 -I/path/to/statcpp/include example.cpp -o example

./example

Using Projections

You can directly process struct members:

#include "statcpp/basic_statistics.hpp"
#include <vector>
 
struct Product {
    std::string name;
    double price;
};
 
int main() {
    std::vector<Product> products = {
        {"Apple", 120.0},
        {"Banana", 80.0},
        {"Orange", 100.0}
    };
 
    // Calculate mean of prices
    double avg_price = statcpp::mean(
        products.begin(),
        products.end(),
        [](const Product& p) { return p.price; }
    );
 
    std::cout << "Average Price: " << avg_price << std::endl;  // 100.0
    return 0;
}

Hypothesis Testing Example

#include "statcpp/parametric_tests.hpp"
#include <vector>
 
int main() {
    std::vector<double> group1 = {23, 21, 19, 24, 20};
    std::vector<double> group2 = {31, 28, 30, 29, 32};
 
    // Two-sample t-test
    auto result = statcpp::t_test_two_sample(
        group1.begin(), group1.end(),
        group2.begin(), group2.end()
    );
 
    std::cout << "t-statistic: " << result.statistic << std::endl;
    std::cout << "p-value: " << result.p_value << std::endl;
    std::cout << "Degrees of Freedom: " << result.df << std::endl;
 
    if (result.p_value < 0.05) {
        std::cout << "Significant difference (p < 0.05)" << std::endl;
    }
 
    return 0;
}

Module List

Module	Header File	Description
Basic Statistics	`basic_statistics.hpp`	Mean, median, mode, etc.
Dispersion	`dispersion_spread.hpp`	Variance, standard deviation, range, etc.
Order Statistics	`order_statistics.hpp`	Quartiles, percentiles, etc.
Distribution Shape	`shape_of_distribution.hpp`	Skewness, kurtosis
Correlation & Covariance	`correlation_covariance.hpp`	Correlation coefficients, covariance
Frequency Distribution	`frequency_distribution.hpp`	Histogram, frequency tables
Special Functions	`special_functions.hpp`	Gamma function, error function, etc.
Random Generation	`random_engine.hpp`	Random number generation engine
Continuous Distributions	`continuous_distributions.hpp`	Normal, t distribution, etc.
Discrete Distributions	`discrete_distributions.hpp`	Binomial, Poisson distribution, etc.
Estimation	`estimation.hpp`	Confidence interval calculation
Parametric Tests	`parametric_tests.hpp`	t-test, z-test, etc.
Nonparametric Tests	`nonparametric_tests.hpp`	Wilcoxon test, etc.
Effect Size	`effect_size.hpp`	Cohen's d, etc.
Resampling	`resampling.hpp`	Bootstrap, etc.
Power Analysis	`power_analysis.hpp`	Sample size calculation
Linear Regression	`linear_regression.hpp`	Simple, multiple regression
ANOVA	`anova.hpp`	Analysis of variance
GLM	`glm.hpp`	Logistic regression, etc.
Distance Metrics	`distance_metrics.hpp`	Euclidean distance, etc.
Numerical Utilities	`numerical_utils.hpp`	Numerical computation helpers

Additional modules include: multivariate analysis, time series analysis, clustering, survival analysis, and more.

For details, see API Reference.

Documentation

Guides

Installation - Installation methods and environment setup
Usage - Basic usage and common specifications
Examples - Practical code examples
API Reference - Overview of all modules and functions
Building and Testing - How to build tests and examples
Contributing Guide - How to contribute to the project
Changelog - Version history

API Documentation

Detailed API documentation can be generated with Doxygen:

# Install Doxygen
brew install doxygen  # macOS
sudo apt-get install doxygen  # Ubuntu/Debian
 
# Generate documentation
./generate_docs.sh
 
# Open in browser
open doc/html/index.html  # macOS
xdg-open doc/html/index.html  # Linux

Tested Environments

macOS + Apple Clang 17.0.0
macOS + GCC 15 (Homebrew)
Ubuntu 24.04 ARM64 + GCC 13.3.0

Development Purpose

During C++ development work, I often needed to perform statistical calculations and had accumulated a fair amount of code. I gathered these scattered pieces of code and created a simple statistics library as a header-only collection of functions.

The target programming language is C++17 with OS-independent code in mind. A Rust version is also planned.

License

This project is released under the MIT License. See the [LICENSE](LICENSE) file for details.

Contributing

Contributions to the project are welcome. Bug reports, feature requests, and pull requests are all appreciated.

For details, see Contributing Guide.

Support

Issue: Bug reports and feature requests via GitHub Issues
Discussion: Questions and discussions via GitHub Discussions

Acknowledgments

The following tools and AI were used in developing this project:

OpenAI ChatGPT 5.2 - Syntax checking and completeness review of documentation
Claude Code for VS Code Opus 4.5 - Google Test code generation, sample code fixes, refactoring
LM Studio google/gemma-2-27b - Syntax checking and completeness review of documentation
llama.cpp - Integrated build and error log management

Note: This library does not match commercial statistical software in terms of numerical stability and handling of extreme edge cases. When using for research or production environments, we recommend verifying results with other tools.