47,133 repositories indexed
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🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.
Flexible level editor
Tars is a high-performance RPC framework based on name service and Tars protocol, also integrated administration platform, and implemented hosting-service via flexible schedule.
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
High-performance C++ library for multiphysics and multibody dynamics simulations
A C++20 library for fast serialization, deserialization and validation using reflection. Supports JSON, Avro, Boost Serialization, BSON, Cap'n Proto, CBOR, Cereal, Command line interfaces, CSV, Environment variables, flexbuffers, msgpack, parquet, TOML, UBJSON, XML, YAML, yas / msgpack.org[C++20]
Flexible Collision Library
log4cplus is a simple to use C++ logging API providing thread-safe, flexible, and arbitrarily granular control over log management and configuration. It is modelled after the Java log4j API.
A header-only C++ argument parser library. Supposed to be flexible and powerful, and attempts to be compatible with the functionality of the Python standard argparse library (though not necessarily the API).
An Open Flexible Quadrotor Simulator
Fast, flexible and simple GUI.
Header-only C++11 library to encode/decode base64, base64url, base32, base32hex and hex (a.k.a. base16) as specified in RFC 4648, plus Crockford's base32. MIT licensed with consistent, flexible API.
multimodal routing, geocoding, and map tiles
Fast and flexible C++ library for working with OpenStreetMap data.
A flexible command line client for Arch Linux's User Repository
An In-Kernel Solution Based on eBPF / XDP for 5G UPF
Linphone.org mirror for flexisip (git://git.linphone.org/flexisip.git)
A fast and flexible c++ template class for tree data structures
A lightweight, flexible, and safer alternative to chroot and/or Docker.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.