Open-source alternatives to Pystan

This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia

This project is a Python data analysis library and exploratory data analysis framework designed for processing raw datasets. It provides a suite of tools for examining data, identifying anomalies, and applying statistical methods to uncover patterns. The repository functions as a machine learning modeling toolkit and a statistical data modeling suite. It includes predictive algorithms and mathematical models used to analyze relationships between data variables and derive insights from complex datasets. The project covers a broad range of capabilities including data science, machine learning

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 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

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

The Python ensemble sampling toolkit for affine-invariant MCMC

CONTRIBUTORS WELCOME Generalized Additive Models in Python

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Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density

A library for hidden semi-Markov models with explicit durations

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Multiple Pairwise Comparisons (Post Hoc) Tests in Python

InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

BAyesian Model-Building Interface (Bambi) in Python.

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

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…

Python package for Bayesian Machine Learning with scikit-learn API

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Pyro is a deep probabilistic programming library and differentiable probabilistic modeler designed for Bayesian inference. It functions as a probabilistic programming language that allows for the construction of complex graphical models using PyTorch tensors and automatic differentiation. The framework enables the definition of universal probabilistic models as standard Python functions. It integrates deep learning with probabilistic modeling to compute posterior distributions and estimate latent variables through gradient-based optimization and algorithmic solvers. The system provides a pro

Statistical package in Python based on Pandas

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

Survival analysis built on top of scikit-learn