Code
Empirical Inference
The MIT License
AutoML Two-Sample Test
autotst is a Python package for easy-to-use two-sample testing and distribution shift detection.
Empirical Inference
The MIT License
Dingo (Deep Inference for Gravitational-wave Observations)
Dingo (Deep Inference for Gravitational-wave Observations) is a Python program for analyzing gravitational wave data using neural posterior estimation. It dramatically speeds up inference of astrophysical source parameters from data measured at gravitational-wave observatories. Dingo aims to enable the routine use of the most advanced theoretical models in analysing data, to make rapid predictions for multi-messenger counterparts, and to do so in the context of sensitive detectors with high event rates.
Empirical Inference
The MIT License
Omni-Fig: Unleashing Project Configuration and Organization in Python
omni-fig is a lightweight package to help you organize your python projects to make everything clear and easy to understand to collaborators and prospective users, while also offering unparalleled features to accelerate development.
The proposed general-purpose project structure is well suited for both small and large projects, and is designed to be easily extensible to fit your needs. Most importantly, with the powerful configuration system, you never have to worry about any boilerplate code to parse command line arguments, read config files, or even import the top-level project compone...
Empirical Inference
The MIT License
normflows: A PyTorch Package for Normalizing Flows
normflows is a PyTorch implementation of discrete normalizing flows. Many popular flow architectures are implemented. The package can be easily installed via pip. The basic usage is described here, and a full documentation is available as well. A more detailed description of this package is given in out accompanying paper.
