statistical-analysis

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON

▁▅▆▃▅ Git quick statistics is a simple and efficient way to access various statistics in git repository.

📊 The statistical analysis tool for git repositories

Enhancing `{ggplot2}` plots with statistical analysis 📊🎨📣

C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage

Multi-class confusion matrix library in Python

Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

Open-source Python library for statistical analysis of randomised control trials (A/B tests)

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

The foundational library of the Morpheus data science framework

Graphical tool for data manipulation written in C++/Qt

A port of HdrHistogram to Rust

Collection of stats, modeling, and data science tools in Python and R.

R package for DNA methylation analysis

Critical difference diagram with Wilcoxon-Holm post-hoc analysis.

A Bayesian model for compositional single-cell data analysis

Meta.Numerics is library for advanced numerical computing on the .NET platform. It offers an object-oriented API for statistical analysis, advanced functions, Fourier transforms, numerical integration and optimization, and matrix algebra.

C++ Matrix -- High performance and accurate (e.g. edge cases) matrix math library with expression template arithmetic operators

CK automation actions to let users implement portable, customizable and reusable program workflows for reproducible, collaborative and multi-objective benchmarking, optimization and SW/HW co-design:

A collection of statistical analysis tools for your Swift programs.

A web interface for exploring PyMC3 traces

An introductory statistics course for social scientists, using Stata

Query2Report is a simple open source business intelligence platform that allows users to build report/dashboard for business analytics or enterprise reporting

Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

Statistical analysis of data on the command line

The Microbiome Interpretable Temporal Rule Engine