Open-source alternatives to Scikit Survival

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 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

Documentation: Stable, Latest.

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

CONTRIBUTORS WELCOME Generalized Additive Models in Python

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

Multiple Pairwise Comparisons (Post Hoc) Tests in Python

Python Toolkit for Causal and Probabilistic Reasoning

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…

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

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

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

PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

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

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

Exploratory analysis of Bayesian models with Python