FLIP Fluids is a production-grade Blender addon for liquid simulation, integrating a custom C++ FLIP (Fluid Implicit Particle) engine with a comprehensive Python UI. The engine implements advanced numerical methods including level sets, variational pressure solvers, PCG with MIC(0) preconditioning, force fields, whitewater particles, and viscosity solvers, all backed by a vendored Eigen linear algebra library. The Blender addon provides property groups, UI panels, preset management, material libraries, and command-line scripts for baking, rendering, and Alembic/USD export. The architecture is a hybrid: the C++ engine is compiled as a shared library and accessed via C bindings and Python ctypes wrappers, while the addon code follows standard Blender patterns (operators, panels, property groups). Key entry points include the baking operators, simulation domain properties, and the engine's Python bindings in `ffengine/`. External dependencies include Blender (4.5+), Eigen, and optionally Mixbox for color blending. The codebase is well-structured with extensive version compatibility layers, but the vendored Eigen and reliance on Blender's evolving API introduce moderate technical drift.
SwiftNIO HTTP/2 is a Swift library providing HTTP/2 protocol support for SwiftNIO-based applications. It implements the HTTP/2 specification (RFC 7540) and HPACK header compression (RFC 7541) in pure Swift, with no external dependencies beyond SwiftNIO core. The library features a modular architecture separating frame handling, connection state management, stream multiplexing, and header compression. Key components include a connection state machine for managing HTTP/2 connection lifecycle, stream multiplexing logic for creating child channels per HTTP/2 stream, and an HPACK implementation with Huffman coding. The repository includes extensive unit tests, integration tests, performance benchmarks, and fuzz testing to ensure correctness and performance.
This is an R package (`parameters`) from the easystats ecosystem that provides a unified interface for extracting, formatting, and comparing parameters from a vast array of statistical models (lm, glmmTMB, brms, lme4, lavaan, fixest, etc.). The package functions as a model-parameter reporting engine: it takes a fitted model object and returns a tidy data frame with coefficients, standard errors, confidence intervals, p-values, and effect sizes, supporting various inference methods (Wald, profile, bootstrap, Kenward-Roger, Satterthwaite). Beyond basic extraction, it offers standardization, bootstrapping, equivalence testing, p-value functions, dominance analysis, and data reduction (PCA, EFA, clustering). The architecture relies heavily on S3 generic dispatch (e.g., `model_parameters.lm`, `model_parameters.glmmTMB`) and integrates with the easystats stack (`insight` for model introspection, `bayestestR` for Bayesian indices, `datawizard` for data manipulation). Output can be formatted as plain text, markdown, or HTML, making it suitable for both interactive use and automated reporting. The package is actively maintained with extensive test coverage and continuous integration.