Data Science
Showing 36 of 624 projects
R package containing datasets and code examples for the book 'Statistical Analysis of Network Data with R, 2nd Edition'.
A Terraform plugin for managing machine learning compute resources across AWS, GCP, Azure, and Kubernetes with spot instance recovery and auto-termination.
An R package for automatic optimal predictor ensembling via cross-validation with dozens of machine learning algorithms.
An idiomatic Clojure dataframe library that runs on Apache Spark, providing a seamless interface for data processing and machine learning.
An application-oriented Deep Reinforcement Learning framework for real-world decision problems, covering simulation to deployment.
A Jupyter widget for interactive graph visualization using cytoscape.js in notebooks and JupyterLab.
An R package that embeds Julia for high-performance numerical computing, enabling seamless interoperability between R and Julia.
Free online version of the 'Applied Machine Learning Using mlr3 in R' textbook, built with Quarto.
Ruby language bindings for the LIBSVM library, enabling support vector machine (SVM) classification and regression in Ruby.
A Python devkit for loading, exploring, and manipulating the PandaSet, a large-scale autonomous driving dataset with LiDAR, camera, and annotations.
A curated, categorized collection of books about the R programming language for data science, statistics, and visualization.
A curated, categorized collection of books about the R programming language for data science, statistics, and visualization.
A pure Python implementation of Apache Spark's RDD and DStream interfaces.
A Swift library providing probability distributions and statistical functions for probabilistic computing.
A web interface and REST API for classification and regression using Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
Define, run, and deploy big data applications on AWS, OpenStack, and local machines using Docker.
R client for the Elasticsearch HTTP API, enabling data indexing, search, and analysis from R.
An F# type provider that enables seamless interoperability with R packages, offering type-safe access to R functions from .NET.
Python-based implementations of algorithms for learning on imbalanced data.
Course materials for GWU's Data Mining and Machine Learning classes covering preprocessing, modeling, and practical Kaggle applications.
An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
An R driver for Neo4j that enables reading and writing graph data directly from the R environment.
An R driver for Neo4j that enables reading and writing graph data directly from R.
A curated collection of learning resources, R packages, and practical examples for understanding and applying topic modeling techniques.
A fast feature selection algorithm for tree-based models like XGBoost, designed to outperform Boruta in speed and performance.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
R bindings to the libgit2 library, providing programmatic access to Git repositories from R.
A Darcula theme for JupyterLab, modeled after the classic IntelliJ theme, with dark scrollbar support.
An R package for flexibly rearranging, reshaping, and aggregating data, now superseded by tidyr.
A charting library designed for interactive data visualization in F# scripting environments.
An R package for creating and exporting statistical animations to HTML, GIF, video, and PDF formats.
A Python toolkit for text-focused data science on medium-sized datasets, bridging memory and cluster-scale processing.
An R interface to Google's V8 JavaScript and WebAssembly engine for executing JavaScript code within R.
A comprehensive family of R packages for analyzing spatial point pattern data and other spatial data types.
A Python library for building lazy data processing and machine learning workflows that handle datasets larger than memory.
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