the complete solution for node.js command-line programs

Check if a certain debug flag is enabled.

HTTP and HTTPS modules that follow redirects.

HTTP proxying for the masses

Client for prometheus

The string_decoder module from Node core

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.

Jupyter telemetry library

This package provides a spawner for Jupyterhub to spawn notebooks using batch resource managers.

LDAP Authenticator for JupyterHub

This package includes WrapSpawner and ProfilesSpawner, which provide mechanisms for runtime configuration of spawners. The inspiration for their development was to allow users to select from a range of pre-defined batch job profiles, but their operation is completely generic.

The SudoSpawner enables JupyterHub to spawn single-user servers without being root, by spawning an intermediate process via sudo, which takes actions on behalf of the user.

The systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd.

JupyterHub: A multi-user server for Jupyter notebooks

Skigen is a header-only C++ template library for machine learning, built on Eigen. It brings the scikit-learn API — fit(), transform(), predict() — to native C++.

keopscore is the KeOps meta programming engine. This python module should be used through a binder (e.g. pykeops or rkeops).

PyTorch extension for handling deeply nested sequences of variable length.

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

This package provides a set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques.

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.

This package provides a neural network library for PyTorch compatible with the scikit-learn API.

aGrUM is a C++ library for graphical models. It is designed for easily building applications using graphical models such as Bayesian networks, influence diagrams, credal networks, Markov random fields, decision trees, GAI networks, (Factored) Markov decision processes, etc.

Features:

• Dedicated data structures

• Lightweight directed/undirected graphs

• Extensible multidimensional matrix

• Bayesian Network algorithms

• Research tools (random generation, introspection)

• Integration tools (listeners, multiple formats)

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields.

Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters.