A package for time series data processing, classification, clustering, and prediction.

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Statistical bootstrapping library for Julia

Read and write feather files in pure Julia

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Exploratory analysis of Bayesian models with Julia

Starter kit for legendary models

Probabilistic Circuits from the Juice library

Utility package for generating, loading, splitting, and processing Machine Learning datasets

3D computer vision library in Julia

MagNav: airborne Magnetic anomaly Navigation

MADS: Model Analysis & Decision Support

Yet Another XArray-like Julia package

Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.

Julia for Whole-genome Analysis Software

Macros and functions to work with DSGE models.

Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet

A fast and simple CSV parser

A Julia package for numerical computation in quantum information theory

Timeseries in Julia

A Julia package for estimating ARMA-GARCH models.

Pure Julia implementations of single-pass object detection neural networks.

Optimization Problems for Julia

Dive deeper into your data with interactive graphics

Frequency tables in Julia

Build flexible hierarchical multi-instance learning models.

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Julia package for the Apache ECharts v4 visualization library

Julia package to access Twitter API

Stochastic Optimization, Learning, Uncertainty and Sampling

Julia package powering VIVA, our tool for visualization of genomic variation data. Manual:

Tidier data transformations in Julia, modeled after the dplyr/tidyr R packages.

A Julia package for interpretable machine learning with stochastic Shapley values

Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

Sampling from intractable distributions, with support for distributed and parallel methods

Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

Solve Fractional Differential Equations using high performance numerical methods

Spin dynamics and generalization to SU(N) coherent states

High level functions for analyzing the output of simulations

Simulated Method of Moments for Julia