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.
Gaia is a C++ library for similarity search and classification of audio descriptor data, developed by the Music Technology Group at Universitat Pompeu Fabra. It provides a rich set of algorithms for transforming and analyzing feature collections: normalization, PCA, RCA, SVM training/prediction, feature selection, and cleaning. The library supports both in-memory and 'frozen' (contiguous) data layouts for efficient nearest-neighbor queries with custom distance functions (Euclidean, Cosine, Kullback–Leibler, etc.) and a custom filter language for WHERE-clause predicates. Python bindings via SWIG expose the full API, and a suite of command-line tools (gaiamerge, gaiainfo, gaiafreeze) handle dataset management. A built-in classification framework with scripts for SVM grid search, cross-validation, and confusion-matrix generation complements the library. Although designed for music information retrieval (MIR) workflows in conjunction with Essentia audio extractors, the library could be adapted to any domain requiring distance-based search and transformation pipelines on structured point data. The codebase is predominantly an application-level library, with a strong research flavor due to its MIR focus.