PyNNDescent is a Python library for fast approximate nearest neighbor search, implementing the NN-descent algorithm. The core pipeline builds a k-nearest neighbor graph via random projection tree initialization followed by iterative graph refinement, with all hot loops JIT-compiled using Numba with explicit types and fastmath. The public API follows scikit-learn conventions: NNDescent constructor for training, .query() for approximate nearest neighbor queries, .neighbor_graph for extracting the training-set graph, and .update() for incremental insertion. It supports dense and sparse data, multiple distance metrics, and parallel execution via Numba's prange. The library is a dependency of UMAP and OpenTSNE, which consume the training-set neighbor graph for manifold learning and density-based clustering.
Tax-Calculator is a mature, open-source microsimulation library for static (and simple behavioral) analysis of the US federal individual income and payroll tax system. It provides a Python API and a CLI tool (`tc`) that allow users to define policy reforms via JSON parameter files, apply them to microdata samples (CPS, PUF, or TMD), and produce distributional tables, revenue estimates, marginal tax rates, and welfare measures. The core architecture separates policy parameters (`Policy`), taxpayer records (`Records`), and economic adjustments (`GrowFactors`, `GrowDiff`, `Consumption`) from the calculation engine (`Calculator`), which uses Numba JIT compilation for performance. While primarily an application/library with formal releases and CI/CD, it also bundles research data (CPS microdata, grow factors) and includes extensive cross-validation against NBER's TAXSIM-35 model. The codebase is well-structured, heavily tested, and actively maintained, with a clear modular design that supports both CLI and programmatic use.