Julia
Showing 36 of 62 projects
A high-level, high-performance dynamic programming language for technical computing.
A weekly social data project providing real-world datasets for practicing data tidying, visualization, and analysis.
A Python tool for parameterizing, executing, and analyzing Jupyter Notebooks at scale.
Feather is a binary columnar serialization format for data frames, enabling fast and interoperable data sharing between Python, R, and other languages.
A plotting and data visualization system for Julia, implementing the Grammar of Graphics.
A Julia machine learning framework providing a unified interface and meta-algorithms for over 200 models.
A flexible and fast package for in-memory tabular data manipulation and analysis in the Julia programming language.
A curated, categorized directory of packages, libraries, and resources for the Julia programming language.
A curated, categorized directory of Julia packages and resources for scientific computing and high-performance numerical analysis.
A deep learning framework for Julia with GPU support and automatic differentiation using dynamic computational graphs.
A deep learning framework for Julia inspired by Caffe, featuring modular architecture and multiple backends.
A comprehensive Julia package for probability distributions, providing properties, PDFs, sampling, and maximum likelihood estimation.
A static code analyzer for Julia that uses type inference to detect potential bugs and type instabilities without requiring type annotations.
An optimized graph analysis package for Julia, providing simple concrete graph types and an API for custom implementations.
A Julia package for fitting linear and generalized linear models with comprehensive statistical functionality.
A Julia implementation of the scikit-learn API, providing a uniform interface for machine learning models from both Julia and Python ecosystems.
A comprehensive image processing library for Julia, providing tools for loading, manipulating, and analyzing images.
A Julia package providing metaprogramming macros to simplify DataFrame manipulation with a more concise syntax.
A Julia package providing efficient, type-safe implementations of numerous distance metrics and divergences between vectors and matrices.
A Julia package for fitting linear and generalized linear mixed-effects models with maximum likelihood estimation.
A Julia package providing digital signal processing routines including filter design, periodograms, window functions, and estimation.
A Julia package for multivariate statistics and data analysis, including dimension reduction techniques like PCA and LDA.
A Julia package providing standard tools and models for text analysis and natural language processing.
A Julia package providing comprehensive clustering algorithms and validation metrics for data analysis.
A lightweight Julia toolkit for working with time series data, providing efficient data structures and operations.
A simple, multi-language implementation of the Iterative Closest Point algorithm for 3D point cloud registration.
A Julia package for Gaussian process modeling with support for exact inference, MCMC sampling, and non-Gaussian likelihoods.
A comprehensive Julia package implementing a wide range of statistical hypothesis tests for data analysis.
A Julia interface for XGBoost, providing efficient distributed gradient boosting for regression, classification, and ranking.
An R package that embeds Julia for high-performance numerical computing, enabling seamless interoperability between R and Julia.
A Julia package for implementing and applying Markov chain Monte Carlo (MCMC) methods for Bayesian analysis.
A Julia library for representation, inference, and learning in Bayesian networks.
A Julia toolkit for graph-based molecule modeling, cheminformatics analysis, and chemical structure manipulation.
A Julia package providing generic graph types, algorithms, and interfaces inspired by the Boost Graph Library.
A Julia package providing kernel density estimation (KDE) for univariate and bivariate data with flexible bandwidth selection and interpolation.
A Swiss knife collection of utility functions for developing and evaluating machine learning algorithms in Julia.
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