A Julia library for computing tree decompositions and chordal completions of graphs.

A new type of Table to store and simulate data for causal inference in Julia.

Create parameter estimation problems for ODE models

Proceedings of PGM 2024

Recursive causal discovery with Julia

DiscoGrad - automatically differentiate across conditional branches in C++ programs

A LaTeX Beamer theme, forked from the metropolis theme

Bachelor's thesis in Mathematics - MVEX11

language level autograd compiler for Julia

Package for the propagation of representations of low-rank matrices through finite compositions of common operations.

Website for the book "The Elements of Differentiable Programming".

Reverse-mode automatic differentiation in Julia

A small step for dynamics, a giant leap for SciML

An exact Bayesian network structure learning software based on dynamic programming.

Polylogarithm function and related special functions and sequences.

No-overhead breakpoints in Julia

Easily smooth your data in Julia

An implementation of minimal perfect hash function generation as described in Czech et. al. 1992.

Implementations of var-sortability, sortnregress, and chain-orientation as presented in the article "Beware of the Simulated DAG": https://arxiv.org/abs/2102.13647.

We introduce a differentiable approach to phylogenetic tree construction, optimizing tree and ancestral sequences in its original representation itself, thus requiring no prior training data.

Synthetic control methods in Julia

Examine The Causal Structure of Data

Python implementation of the GES algorithm for causal discovery, from the 2002 paper "Optimal Structure Identification With Greedy Search" by David Maxwell Chickering.

Data and functions to support Julia projects based on the book "Regression and Other Stories" by Andrew Gelman, Jennifer Hill and Aki Vehtari.