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Probabilistic inference using the symbolic method.

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

Adds support for parallelization for Taija packages.

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

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

A package for plotting custom symbols from Taija packages.

Connecting MLJ and MLFlow

High Level API Finite Element Methods based on ExtendableGrids and ExtendableFEMBase

Basic structures for finite elements based on ExtendableGrids infrastructure

High performance hybrid Monte Carlo simulation

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

Julia implementation of Modal Decision Trees & Forests, for interpretable classification of spatial and temporal data. Long live Symbolic Learning!!

Measures (metrics) for statistics and machine learning

A Julia package for integration of packages of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

A Julia package for visualizing of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

MPI based parallel for method of moments (MoM) and MLFMA (https://github.com/deltaeecs/MoM_AllinOne.jl.jl)

A Julia package for kernel functions of Method of Moments (MoM) and Multi-level Fast Multipole Algorithm (MLFMA).

Advanced exports management for AutomationLabs.jl

Businees functions for life insurance contract and product management

A simple weather app for the Julia REPL

Time Series Classification algorithms in Julia - Bachelor thesis project

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.

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One package to train them all

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Counting things in julia

Interpretable Machine Learning via Rule Extraction

A Julia package for Generative Topographic Mapping

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Warehouse for dynamical systems identification and control

A powerful, no code solution for control and systems engineering

Dynamical systems identification

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

Implementation of a GNSSReceiver

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