Awesome GitHub RepositoriesStatistical Modeling

SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li

Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s

PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o

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

DoWhy is an open-source Python library for causal inference that structures the entire analysis into a sequential four-step framework: modeling, identification, estimation, and refutation. It treats causal assumptions as explicit, first-class citizens, represented as directed acyclic graphs that can be automatically validated against observed data. The library distinguishes itself by cleanly separating the causal identification problem from statistical estimation, allowing any compatible estimator to be used for a given target estimand. It includes automated refutation testing that validates

Schema.org - schemas and supporting software

这是一个 Python 数据分析库和探索性数据分析框架，专为处理原始数据集而设计。它提供了一套用于检查数据、识别异常并应用统计方法以发现模式的工具。 该仓库作为一个机器学习建模工具包和统计数据建模套件。它包括用于分析数据变量之间关系并从复杂数据集中获取见解的预测算法和数学模型。 该项目涵盖了广泛的功能，包括数据科学、机器学习建模和探索性数据分析。这些功能通过数据操作、数值计算和数据可视化实现。

这是一个面向 .NET 生态系统的科学计算框架，提供了一套全面的数值分析、统计和数学优化库。它作为开发机器学习、数字信号处理和计算机视觉应用的基础工具包。 该框架提供了用于训练和部署预测模型的专用工具包，包括神经网络、支持向量机和决策树。它还通过对实时视觉分析（如对象跟踪和面部特征检测）的深度集成，以及用于捕获和过滤音频及传感器信号的专用数字信号处理库而脱颖而出。 其功能范围扩展到高级矩阵分解和线性代数、概率状态建模和启发式搜索算法。它还涵盖了广泛的数据操作实用程序，从降维和归一化到空间数据组织和科学可视化组件。 该系统包括用于摄像机配置、GPIO 端口管理和专用深度传感硬件的硬件集成控制器。

Fast, flexible and easy to use probabilistic modelling in Python.

Python Toolkit for Causal and Probabilistic Reasoning

Probabilistic programming powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Survival analysis in Python

Statistical package in Python based on Pandas

Exploratory analysis of Bayesian models with Python

This repository serves as an educational resource and structured curriculum for performing statistical analysis using Python. It provides a comprehensive guide to the scientific computing workflow, focusing on the practical application of data cleaning, numerical modeling, and distribution visualization. The tutorial covers the end-to-end process of transforming raw tabular data into actionable insights. It demonstrates how to manipulate structured datasets through merging and aggregation, perform descriptive and inferential statistical calculations, and fit regression models to evaluate rela

This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine learning and testing workflows. It functions as a privacy-preserving platform that models the underlying statistical distributions of source data to produce new records that maintain the original statistical properties and structural integrity. The tool distinguishes itself by utilizing CPU-optimized statistical sampling, allowing for high-performance data generation on standard hardware without the need for specialized graphics processing units. It employs a configuration-driv

Survival analysis built on top of scikit-learn

CONTRIBUTORS WELCOME Generalized Additive Models in Python

Notice: patsy is no longer under active development. As of August 2021, Matthew Wardrop (@matthewwardrop) and Tomás Capretto (@tomicapretto) have taken on responsibility from Nathaniel Smith (@njsmith) for keeping the lights on, but no new feature development is planned. The spiritual successor…

Google's Causal Impact Algorithm Implemented on Top of TensorFlow Probability.