Search Packages

blosc2

A high-performance library for compressed ndarrays, with a flexible computational engine

tripy

Simple polygon triangulation algorithms in pure python

bhv

Boolean Hypervectors with various operators for experiments in hyperdimensional computing (HDC).

probnode

Probability expression representation and calculation library

hbayesdm

Python interface for hBayesDM, hierarchical Bayesian modeling of RL-DM tasks

latentcor

Fast Computation of Latent Correlations for Mixed Data

fastgrouper

A package for applying efficient groupby operations.

fairtool

A CLI tool for FAIR processing of computational materials science data.

thoad

**thoad** (Torch High Order Automatic Differentiation) is a lightweight reverse-mode autodifferentiation engine written entirely in Python that works over PyTorch’s computational graph to compute **high order partial derivatives**. Unlike PyTorch’s native autograd - which is limited to first-order native partial derivatives - **thoad** is able to performantly propagate arbitray-order derivatives throughout the graph, enabling more advanced gradient-based computations.