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Clapeyron
Framework for the development and use of fluid-thermodynamic models
Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC. -
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AppleAccelerate.jl
Julia interface to the macOS Accelerate framework
Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions. The performance is significantly better than using standard libm functions in some cases, though there does appear to be some reduced... -
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GDAL.jl
Thin Julia wrapper for GDAL - Geospatial Data Abstraction Library
Julia wrapper for GDAL - Geospatial Data Abstraction Library. This package is a binding to the C API of GDAL/OGR. It provides only a C style usage, where resources must be closed manually, and datasets are pointers. Other packages can build on top of this to provide a more Julian user experience. See for example ArchGDAL.jl. Most users will want to use ArchGDAL.jl instead of using GDAL.jl directly. -
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ReTest.jl
Testing framework for Julia
ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for... -
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LIFELINES
Survival analysis in Python
LIFELINES is a pure Python library for survival analysis, a statistical field focused on modeling time until an event occurs. It can be used for traditional cases like medical survival time, but also for business and product questions such as churn, subscription length, equipment failure, and customer retention. The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common... -
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MLJBase.jl
Core functionality for the MLJ machine learning framework
Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ. -
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DynamicalSystems.jl
Award winning software library for nonlinear dynamics timeseries
DynamicalSystems.jl is an award-winning Julia software library for nonlinear dynamics and nonlinear time series analysis. To install DynamicalSystems.jl, run import Pkg; Pkg.add("DynamicalSystems"). To learn how to use it and see its contents visit the documentation, which you can either find online or build locally by running the docs/make.jl file. DynamicalSystems.jl is part of JuliaDynamics, an organization dedicated to creating high-quality scientific software. All implemented algorithms... -
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data-table-filters
Faceted filters, sorting & infinite scroll for React data tables
data-table-filters is a frontend utility designed to simplify the implementation of advanced filtering capabilities in data tables within web applications. It provides a set of reusable components and hooks that allow developers to quickly add filtering, sorting, and search functionality to tabular data. The library is built with modern JavaScript frameworks in mind, ensuring compatibility with React-based applications. It emphasizes performance and usability, enabling efficient handling of... -
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mapcn
Beautiful map components, 100% Free, Zero config, one command setup
mapcn is a research-oriented project centered on mapping continuous control in reinforcement learning to structured policies using neural networks. It explores how high-dimensional action spaces can be decomposed into structured primitives that can be learned, composed, and reused across different tasks. The core idea is to enable agents to generalize learned behavior by representing continuous control policies in a compact, interpretable form that preserves smoothness and controllability.... -
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Obsidian Visual Skills Pack
Generate Canvas, Excalidraw, and Mermaid diagrams from text
LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking... -
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Koito
Koito is a modern, themeable scrobbler
Koito is a lightweight, self-hostable collaborative knowledge base and note-taking platform designed to help teams and individuals capture, organize, and link ideas with the reliability of a local or private server rather than relying on managed SaaS tools. It combines concepts from wikis, Zettelkasten systems, and modern note apps by letting you create interconnected notes, bidirectional links, and structured collections that mirror how knowledge naturally grows. The interface supports... -
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GenStage
Producer and consumer actors with back-pressure for Elixir
GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts,... -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets,... -
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Ahoy
Simple, powerful, first-party analytics for Rails
Ahoy is a first-party analytics library built primarily for Ruby on Rails, designed to let applications track visits and events in a clean, integrated way rather than relying on third-party tooling. It stores data in your own database by default, which gives developers full control over what data is captured, how it's processed, and how it’s used, sidestepping privacy concerns of external analytics providers. The library supports Rails, JavaScript, and native apps, making it flexible across... -
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Siddhi Core Libraries
Stream Processing and Complex Event Processing Engine
Fully open source, cloud-native, scalable, micro streaming, and complex event processing system capable of building event-driven applications for use cases such as real-time analytics, data integration, notification management, and adaptive decision-making. Event processing logic can be written using Streaming SQL queries via graphical and source editors, to capture events from diverse data sources, process and analyze them, integrate with multiple services and data stores, and publish... -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units).... -
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Measurements.jl
Error propagation calculator and library for physical measurements
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have... -
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions... -
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Apache Doris
MPP-based interactive SQL data warehousing for reporting and analysis
Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL... -
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Apache Polaris
Apache Polaris, the interoperable, open source catalog
Apache Polaris is an open-source metadata catalog and data management service designed to manage Apache Iceberg tables in modern data lakehouse environments. It provides a centralized catalog that allows multiple compute engines and analytics systems to interact with the same datasets through a standardized interface. By implementing the Iceberg REST catalog API, Polaris enables distributed data platforms to access shared table metadata without tightly coupling storage systems and query... -
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Excalidraw MCP
Fast and streamable Excalidraw MCP App
Excalidraw-MCP is an open-source Model Context Protocol (MCP) application and server that connects the visual power of Excalidraw’s hand-drawn diagram editor with AI-driven workflows, enabling agents like Claude, ChatGPT, VS Code, and other MCP-compatible hosts to generate and manipulate diagrams programmatically. Rather than being just a static whiteboard, Excalidraw-MCP serves diagrams in real time using an MCP backend and streams interactive visual output back to the client, letting AI... -
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Superstruct
A simple and composable way to validate data in JavaScript
This project is a lightweight validation library for JavaScript and TypeScript that helps you define data “shapes” and validate runtime values against them. Instead of relying only on compile-time typing, it focuses on the reality that many apps receive unknown input from APIs, forms, and external systems, and those values need runtime checks. Its API is intentionally familiar if you’ve used TypeScript, Flow, Go structs, or GraphQL schemas, but the output is oriented around runtime... -
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AWS SDK for pandas
Easy integration with Athena, Glue, Redshift, Timestream, Neptune
aws-sdk-pandas (formerly AWS Data Wrangler) bridges pandas with the AWS analytics stack so DataFrames flow seamlessly to and from cloud services. With a few lines of code, you can read from and write to Amazon S3 in Parquet/CSV/JSON/ORC, register tables in the AWS Glue Data Catalog, and query with Amazon Athena directly into pandas. The library abstracts efficient patterns like partitioning, compression, and vectorized I/O so you get performant data lake operations without hand-rolling... -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
NVIDIA Merlin is an open-source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see NVIDIA... -
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GeoServer is an open source software server written in Java that allows users to share and edit geospatial data. Designed for interoperability, it publishes data from any major spatial data source using open standards: WMS, WFS, WCS, WPS and REST
