statcpp CLI

Bring statistics to your UNIX pipeline. Also runs on Windows (MSVC / MinGW)

日本語版 / Japanese

Development Purpose

A command-line statistics tool built on the C++17 header-only statistics library statcpp. Just as awk handles text processing and jq handles JSON, statcpp handles statistical analysis. The goal is a single binary with zero dependencies and instant startup that fits naturally into your data analysis workflow.

Features

• 16 categories, 94 commands (from descriptive statistics to survival analysis)
• Automatic CSV / TSV detection, stdin pipe support
• Text / JSON / quiet output modes
• C++17 single binary, startup in milliseconds

Build

git clone <repository-url>
cd statcppCLI
cmake -B build && cmake --build build

Built binary: build/statcpp

Windows (MSVC):

git clone <repository-url>
cd statcppCLI
cmake -S . -B build
cmake --build build --config Release

Built binary: build\Release\statcpp.exe

Install

# Install to /usr/local/bin/statcpp
sudo cmake --install build

# Install to a custom directory
cmake --install build --prefix ~/.local

Windows:

cmake --install build --config Release --prefix C:\Users\<user>\AppData\Local\statcpp

Prerequisites

• CMake 3.20+
• C++17 compatible compiler (GCC 9+, Clang 10+, Apple Clang 12+, MSVC 2019+)

statcpp, gflags, and nlohmann/json are automatically downloaded by CMake.

Quick Start

# Display all basic statistics at once
statcpp desc summary data.csv --col price

# Mean (via pipe)
cat data.csv | statcpp desc mean --col value

# Directly from stdin
printf '1\n2\n3\n4\n5\n' | statcpp desc mean --noheader --col 1

# Inline data (space or comma-delimited)
echo '1 2 3 4 5' | statcpp desc mean --noheader --col 1 --row

# t-test (two-group comparison)
statcpp test t data.csv --col group1,group2

# Correlation analysis
statcpp corr pearson data.csv --col x,y

# Regression analysis
statcpp reg simple data.csv --col x,y

# JSON output
statcpp desc summary data.csv --col price --json

# Numeric value only (for piping)
statcpp desc mean data.csv --col price --quiet

# Power analysis (no CSV required)
statcpp power t-one --effect 0.5 --n 30

Output Example

$ statcpp desc summary data.csv --col value
  Count:        5
  Mean:         30
  Std Dev:      15.8114
  Min:          10
  Q1:           20
  Median:       30
  Q3:           40
  Max:          50
  Skewness:     0
  Kurtosis:     -1.2

Categories

Category	Description	Commands
desc	Descriptive statistics	17
test	Statistical tests	12
corr	Correlation and covariance	5
effect	Effect size	6
ci	Confidence intervals	5
reg	Regression analysis	5
anova	Analysis of variance	5
resample	Resampling	6
ts	Time series analysis	8
robust	Robust statistics	7
survival	Survival analysis	3
cluster	Clustering	3
multiple	Multiple testing correction	3
power	Power analysis	3
glm	Generalized linear models	2
model	Model selection	4

Shortcuts: mean, median, mode, sd, var, summary, range, iqr, cv, skewness, kurtosis, quartiles, gmean, hmean, ttest, pearson, spearman, kendall

Documentation

• Command Reference - Options and usage for all commands
• Test Reference - Execution examples and output verification for all commands
• Design and Developer Guide - Architecture and development information
• Test Verification Results - Execution results for all commands

Test

# Unit tests
cmake -B build -DGTEST=true && cmake --build build && ctest --test-dir build --verbose

# E2E tests (macOS / Linux only)
cd test/e2e && bash run_e2e.sh

# Verify all reference examples (macOS / Linux only)
bash docs/run_reference.sh

Tested Environments

• macOS + Apple Clang 17.0.0
• macOS + GCC 15 (Homebrew)
• Ubuntu 24.04 ARM64 + GCC 13.3.0
• Windows 11 ARM64 + MSVC 2022 (via Parallels)

License

This project is released under the MIT License. See the LICENSE file for details.

Dependencies

Library	License
statcpp	MIT License
gflags	BSD 3-Clause License
nlohmann/json	MIT License

Acknowledgments

This project was developed using the following tools and AI:

• Claude Code for VS Code Opus 4.6 - Terminology consistency in documentation, identifying missing explanations, test code generation and result verification
• OpenAI ChatGPT 5.2 - Terminology consistency in documentation, identifying missing explanations

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Note: This software does not have the same level of numerical stability or edge case handling as commercial statistical software. When using for research or production, we recommend verifying results with other tools.