Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia

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

ADRIA: Adaptive Dynamic Reef Intervention Algorithms. A multi-criteria decision support platform for informing reef restoration and adaptation interventions.

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

Ecosystem of numerical methods for microbial kinetics data analysis, from preprocessing to result interpretation.

A Julia machine learning framework

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A package for plotting custom symbols from Taija packages.

A package for wrapping iterative MLJ models in a control strategy

Optimally tuned ridge regression when features can be partitioned into groups.

Interpretable Machine Learning via Rule Extraction

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

TreeParzen.jl, a pure Julia hyperparameter optimiser with MLJ integration

A Julia toolbox with resampling methods to correct for class imbalance.

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

A pure Julia implementation of the Targeted Minimum Loss-based Estimation

An unofficial Julia wrapper for the RAPIDS.ai ecosystem using PythonCall.jl

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Connecting MLJ and MLFlow

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A Julia package for Generative Topographic Mapping

Measures (metrics) for statistics and machine learning

Core functionality for the MLJ machine learning framework

A decision-making framework for the cost-efficient design of experiments, balancing the value of acquired experimental evidence and incurred costs.

Utilities to test implementations of the MLJ model interface and provide integration tests for the MLJ ecosystem

A package with exported learning networks that combine resampling methods from Imbalance.jl and classification models from MLJ

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This is a dimensionality reduction algorithm which has the goal of maintaining interpretability i.e we eliminate variables directly from potential models that don't seem to add any predictive power.

A Julia package for modelling Algorithmic Recourse Dynamics.

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Adds support for parallelization for Taija packages.

Empirical Bayes shrinkage in Julia

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Invariant Causal Prediction in pure Julia

Fast, scalable and flexible Outlier Detection with Julia

A powerful, no code solution for control and systems engineering

Advanced exports management for AutomationLabs.jl

Warehouse for dynamical systems identification and control

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Julia Toolkit with fairness metrics and bias mitigation algorithms