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Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Probabilistic programming via source rewriting
Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Create Your Own Metropolis-Hastings Markov Chain Monte Carlo Algorithm for Bayesian Inference (With Python)
Bayesian Modeling and Probabilistic Programming in Python
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
Solve ODEs fast, with support for PyMC
Examples of PyMC models, including a library of Jupyter notebooks.
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning a…
Global documentation for the Julia SciML Scientific Machine Learning Organization
Python module that makes working with XML feel like you are working with JSON
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io
Don't worry about DrugBank licensing - write code that knows how to download it automatically
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"
Headless version of drawio-desktop
Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation