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

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

Native Julia I/O package to work with CERN ROOT files objects (TTree and RNTuple)

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

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

A Julia package for estimating ARMA-GARCH models.

Dive deeper into your data with interactive graphics

Optimization Problems for Julia

Build flexible hierarchical multi-instance learning models.

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

Stochastic Optimization, Learning, Uncertainty and Sampling

Julia package to access Twitter API

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

Julia package for the Apache ECharts v4 visualization library

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

OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.

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.

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

Imputation methods for missing data in julia

Fast Fourier transforms of MPI-distributed Julia arrays