#statistics

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

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

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Go language library for reading and writing Microsoft Excel™ (XLAM / XLSM / XLSX / XLTM / XLTX) spreadsheets

Gajae Code MVP

Grab your DNS logs, detect anomalies, and finally understand what's happening on your network. The missing piece between DNS servers and your data stack.

Next Generation Machine Learning, Statistics and Deep Learning in PURE Rust

PyAutoFit: Classy Probabilistic Programming

Library for generating various random variables

Statistical Machine Intelligence & Learning Engine

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

sportsdataverse python package

Statistical computation library for Rust

A Julia package for probability distributions and associated functions.

Additional linear models including instrumental variable and panel data models that are missing from statsmodels.

StatsPAI is the first Agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, Skills, an MCP server, and R/Stata parity validation.

Fast, Complete NumPy for TypeScript & JavaScript

🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.