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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Starter kit for legendary models

Probabilistic Circuits from the Juice library

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

MADS: Model Analysis & Decision Support

MagNav: airborne Magnetic anomaly Navigation

3D computer vision library in Julia

Experimental Julia implementation of the Amazon Braket SDK

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

Julia for Whole-genome Analysis Software

Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet

Timeseries in Julia

A Julia package for numerical computation in quantum information theory

A fast and simple CSV parser

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

Julia DataFrames serialization format

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

Build flexible hierarchical multi-instance learning models.

Julia package to access Twitter API

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

Stochastic Optimization, Learning, Uncertainty and Sampling

Julia package for the Apache ECharts v4 visualization library

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

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

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.

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.

Move data to and from the clipboard in Julia

Spin dynamics and generalization to SU(N) coherent states

Simulated Method of Moments for Julia

Flexible and performant GEMM kernels in Julia

🧬High-performance genetics- and genomics-related data visualization using Makie.jl

Imputation methods for missing data in julia