Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

A Julia package for modelling Algorithmic Recourse Dynamics.

A package for Joint Energy Models and Energy-Based Models in Julia.

A package for plotting custom symbols from Taija packages.

A package for enabling interoperability between Python and R machine learning models with Taija.

Base package that ships symbols and functionality that is relevant to all or multiple packages in the ecosystem

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Adds support for parallelization for Taija packages.