Open-source alternatives to Stat Rethinking 2022

This repository provides a collection of Python implementations for causal inference, designed to estimate the impact of specific interventions using observational data. It serves as a statistical toolkit for researchers to isolate causal signals from complex confounding factors in data sets that lack experimental control. The framework enables the application of rigorous methodologies to study health determinants and evaluate policy interventions. By utilizing structural causal modeling and directed acyclic graphs, the library allows users to map causal dependencies and identify the necessar

This project is an educational resource and toolkit for implementing Bayesian estimation and Kalman filters in Python. It provides a framework for constructing linear and non-linear filters to estimate the state of dynamic systems by combining noisy sensor data with mathematical process models. The library focuses on probabilistic state estimation, utilizing recursive Bayesian updating and state-space mathematical modeling to refine beliefs about system states. It includes utilities for simulating dynamic systems, allowing users to generate synthetic trajectories and sensor observations to va

This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for performing statistical inference and quantifying uncertainty through a probabilistic programming workflow using Python. The resource employs a computation-first pedagogy, teaching Bayesian methods and parameter estimation through executable code and simulations instead of formal mathematical notation. It provides a practical approach to implementing Markov Chain Monte Carlo sampling to estimate posterior distributions. The content covers building probabilistic models, integrating e

CausalML is a machine learning library for causal inference, providing tools to estimate treatment effects and causal impacts using experimental and observational data. It functions as a framework for uplift modeling and the estimation of heterogeneous treatment effects to distinguish causation from correlation. The library focuses on identifying how different user segments respond to specific interventions. This includes calculating the incremental gain of target metrics to optimize marketing campaigns, targeting high-response customer segments, and personalizing user engagement through the

LimiX is a tabular foundation model and a suite of tools for structured data, providing a transformer-based system for classification, regression, and data generation. It includes a causal inference engine to determine cause-and-effect relationships, a synthetic data generator, and a framework for filling missing dataset values through feature context prediction. The project optimizes tabular inference through a high-performance system that uses ensemble-based sample retrieval to increase prediction speed and accuracy on high-specification hardware. It further distinguishes itself by using tr

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This project is a technical interview repository and a curated collection of study guides designed for backend, frontend, and full-stack engineering evaluations. It provides a JavaScript interview guide and a comprehensive question bank of solved coding challenges and algorithmic problems to prepare developers for job screenings. The resource extends beyond core language skills to include a frontend development syllabus covering DOM fundamentals and accessibility, a backend architecture reference for server-side concepts and distributed systems, and a full-stack study resource for the MERN st

Visual Insights is an automated exploratory data analysis platform and causal inference tool designed to discover patterns and cause-and-effect relationships within datasets. It functions as an interactive data visualization library using a grammar-of-graphics approach to generate multi-dimensional charts and dashboards. The project distinguishes itself through a natural language interface that translates plain-text questions into data answers and visualizations via a language model. It provides a specialized framework for causal discovery and inference, allowing users to identify variable li

This repository serves as an educational resource for Bayesian statistical modeling, providing a collection of instructional examples that translate theoretical concepts into executable Python code. It functions as a computational framework for performing statistical inference and parameter estimation, designed to help users learn and apply probabilistic programming techniques through interactive documentation. The project utilizes a probabilistic programming framework to define statistical models as directed acyclic graphs, enabling automated inference through advanced sampling algorithms. B

This project provides a comprehensive educational framework designed to structure the acquisition of skills in machine learning and artificial intelligence. It serves as a centralized repository of learning paths that guide students through the core concepts and practical applications of modern artificial intelligence, ranging from foundational theory to advanced professional specializations. The platform distinguishes itself through a modular architecture that segments broad technical fields into discrete, manageable learning paths. By utilizing a hierarchical curriculum, it organizes comple

tui.chart is a JavaScript data visualization library and multi-type charting engine used to render interactive statistical charts. It functions as a responsive charting framework and real-time data visualizer, utilizing HTML5 Canvas for high-performance rendering of diverse datasets. The library provides a wide array of visual formats, including linear, circular, grid-based, hierarchical, and statistical chart types. This covers everything from standard bar, line, and pie charts to more specialized visualizations such as radar charts, treemaps, bubble charts, and box plots. The engine includ

This project is a fitness activity dashboard and data pipeline that aggregates running statistics and routes from external sports platforms into a personal visual summary. It includes a fitness data migration tool for consolidating records between different sports services and local storage, alongside a data importer for processing GPX, TCX, and FIT files. The system features an automated activity sync pipeline that periodically fetches fitness data and redeploys a static dashboard to a hosting platform. It also provides a keyboard-driven terminal user interface for filtering and viewing runn

Plotnine is a data visualization library for Python based on the Grammar of Graphics. It serves as a declarative statistical plotting framework and multi-panel plotting engine, allowing users to create complex charts by mapping data variables to visual properties such as position, color, and size. The project is distinguished by its use of a layered composition model and a statistical transformation engine that performs aggregations and computations before rendering visuals. It features a comprehensive system for multi-panel faceting, which enables the splitting of a single visualization into

This project is a Go language learning guide and implementation map designed to help developers transition from Node.js to Go. It provides a series of side-by-side code comparisons that contrast identical logic implemented in both languages to illustrate syntax and runtime differences. The repository serves as a tutorial for Go concurrency, specifically comparing the use of channels and goroutines against JavaScript promises. It also includes a performance benchmarking suite to measure and compare the execution speed of Go against Node.js for the same operations. The guide covers systems pro

tui.chart is a statistical charting engine and data visualization library designed to render interactive charts and graphs for complex numerical datasets. It functions as a responsive charting framework capable of producing various statistical representations, including line, bar, scatter, bubble, and treemap charts. The library distinguishes itself through an interactive visualization system that supports synchronized tooltips across multiple charts, range-based zooming, and data series selection. It includes a layout engine that automatically adjusts chart dimensions and animations based on

This project is a Python machine learning library and data science toolkit designed for building predictive models and analyzing complex datasets. It provides a collection of implementations for common supervised and unsupervised algorithms using the Scikit-Learn framework. The toolkit includes a predictive modeling suite for generating predictions from historical data and a statistical analysis framework for applying Bayesian modeling and causality tests. It also features a data visualization suite based on Matplotlib for rendering static charts and graphs to interpret classifier boundaries

This is a video course that teaches JavaScript from scratch, covering fundamentals like variables, functions, scope, closures, and data types through 88+ lessons organized into 15 modules. The course uses a live-coding pedagogy where students code alongside pre-recorded video lessons, with each lesson providing starter and solution files for self-paced practice. The curriculum focuses on browser-native APIs, teaching DOM manipulation, event handling, and data fetching without external frameworks. Students build real-world projects like image galleries, drawing tools, and audio visualizers thr

This project is a comprehensive educational curriculum designed to build proficiency across modern infrastructure, cloud-native technologies, and systems administration. It functions as a reference library and interview preparation resource, offering a structured collection of conceptual questions, practical coding challenges, and hands-on scenarios that cover the full spectrum of software delivery and operational workflows. The repository distinguishes itself through a modular, domain-specific structure that links instructional problem statements with verified implementation examples. By emp

This project provides a collection of pre-built administrative dashboard templates and a comprehensive user interface kit designed for constructing professional management applications. It is built upon the Bootstrap framework, offering a set of responsive themes and layout blocks that adapt to various screen sizes. The framework distinguishes itself by providing modular, reusable building blocks that allow developers to assemble complex administrative interfaces. It includes specialized components for data grid management, enabling the organization, sorting, and filtering of large datasets,

This project is a machine learning education resource consisting of Python implementations of statistical learning models and data analysis examples from a core textbook. It serves as a statistical modeling library that provides the code necessary to implement linear regression, classification, and unsupervised learning techniques for academic data analysis. The repository is structured as a reference-driven implementation, with a directory layout that mirrors the chapter and section hierarchy of the associated academic publication. It includes a set of scripts and notebooks designed to gener

This project provides an interactive notebook environment designed for executing C# code alongside instructional text. It functions as a platform for data science and programming education, allowing users to manipulate tabular data, visualize statistical results, and prototype machine learning workflows within a unified document format. The environment utilizes a kernel-based execution model that maintains persistent state and variable scope across notebook cells. By leveraging a language-agnostic protocol and standardized messaging, it synchronizes code execution between the editor and the b

XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

This project is a comprehensive pandas data analysis tutorial and instructional guide designed for learning data manipulation and analysis. It serves as a tabular data processing guide and a manual for time series analysis, providing a structured approach to cleaning, merging, and transforming datasets. The repository functions as a data feature engineering course, providing tutorials on constructing and selecting dataset features to improve machine learning model performance. It also includes a vectorized data operations guide for performing element-wise mathematical computations and matrix

This repository is a collection of common computer science algorithms and data structures implemented across multiple programming languages. It serves as an educational resource for students and developers to study fundamental computational logic and software engineering patterns through practical, version-controlled code examples. The project distinguishes itself by decoupling abstract problem definitions from their specific language implementations. This structure allows for direct cross-language comparison, enabling users to analyze how different syntax and programming paradigms handle ide

This is a web-based adaptive touch typing trainer designed to help users master touch typing through personalized exercises. It functions as a heuristic typing tutor that generates custom lessons based on individual keystroke statistics and accuracy data. The application uses adaptive skill modeling to prioritize the most difficult keys for a user and predicts the number of lessons required to reach specific target speeds. A performance dashboard transforms raw typing statistics into graphical representations to visualize learning growth and proficiency over time. The system covers real-time

SHAP is an explainable AI toolkit that provides a game theoretic framework for interpreting machine learning model predictions. It functions as a feature attribution engine, decomposing model outputs into the sum of individual feature effects to clarify how specific input variables influence a final decision. By assigning importance values to these inputs, the library enables users to understand the logic behind complex predictive models. The project distinguishes itself through its versatility and specialized calculation methods. It operates as a model-agnostic diagnostic library, capable of