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Code used for simulations and analysis for "Targeted incentives for social tipping in heterogeneous networked populations"
A Python package for causal inference in quasi-experimental settings
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
A web application for interactive visual editing of Graphviz graphs described in the DOT language.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
A repository of links with advice related to grad school applications, research, phd etc
Code for the paper "Joint Causal Inference from Multiple Contexts" (JMLR 2020)
Code for UAI 2018 paper by Forré & Mooij (causal discovery with mSCMs using sigma-separation)
Development version of phaseR, an R package for phase plane analysis of one- and two-dimensional autonomous ODE systems
R package to accompany Modeling with Data and Differential Equations Using R textbook.
Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
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…
Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distribution…
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
A Python library for efficient Bayesian modeling with deep learning
An implementation of the Grammar of Graphics in R
Bayesian Analysis of Graphical Models
Supplementary Materials of Dablander & Hinne (2019).