TensorFlow Probability (TFP) is a production-grade library for probabilistic modeling and Bayesian inference. It provides a comprehensive suite of probability distributions, bijectors (normalizing flows), MCMC kernels (HMC, NUTS, SMC, etc.), variational inference, Gaussian processes, structural time series, and Bayesian optimization. The library supports TensorFlow, JAX, and NumPy backends through a rewrite system, enabling seamless switching between hardware accelerators. The architecture is modular: each distribution, bijector, and kernel inherits from well-defined abstract base classes with `parameter_properties` for automatic shape and constraint inference. Key entry points include `tfp.distributions`, `tfp.bijectors`, `tfp.mcmc`, `tfp.vi`, `tfp.sts`, and `tfp.experimental` for cutting-edge methods. The code is heavily tested across all backends, with property-based tests and numerical stability checks. While primarily an application library, it also includes research-grade components in `discussion/` and `spinoffs/` (Inference Gym, AutoBNN, FunMC) that serve as benchmarks and experimental prototypes.
PyMC is a production-grade Bayesian modeling and probabilistic programming library built on PyTensor. It exposes a model-building API (pm.Model, pm.Normal, pm.sample), automatic log-probability derivation through graph rewriting, MCMC samplers (NUTS, HMC, Metropolis, Slice, DEMetropolis), Sequential Monte Carlo, variational inference (ADVI, FullRankADVI, SVGD), Gaussian processes, ODE support, and data containers (pm.Data, Minibatch). Users construct symbolic random-variable graphs using PyTensor; PyMC rewrites those graphs into measurable IR, computes log-probabilities and transforms, compiles sampling or variational objective functions, and returns posterior traces as xarray/ArviZ DataTree objects. Although the library primarily serves research and statistical workflows, the repository itself is an application/library, not a data-research pipeline. Architecturally, this is a library, not a service: there are no daemons or network endpoints. The entry points are Python APIs in pymc/model/core.py, pymc/sampling/mcmc.py, pymc/distributions/, pymc/step_methods/, and pymc/variational/. The core data flow is model specification -> PyTensor graph -> fgraph_from_model -> logprob rewrites -> compiled sampling functions -> trace backends (ndarray, zarr) -> to_inference_data for posterior analysis. External dependencies include NumPy, SciPy, PyTensor, ArviZ, and optional JAX/BlackJAX/NumPyro, Numba, Zarr, Rich, and Marimo. The codebase has an extensive test suite and CI, with some experimental areas such as pymc.dims and ongoing deprecations. Maintenance is active, with recent commits and rigorous CI. Technical drift is medium: most of the stack tracks current Python, but the Zarr backend is not yet ported to Zarr 3 and several optional integrations carry compatibility shims. Overall craftsmanship is strong but not exceptional: the code is deeply engineered, though its size and internal coupling make individual components less trivially extractable, and some deprecated APIs and experimental modules add friction.
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.
Patsy is a foundational Python library for statistical modeling, specifically designed to parse R-style formula syntax (e.g., 'y ~ x1 + x2') and construct design matrices (numerical arrays) for regression and machine learning algorithms. It serves as a critical dependency for the statsmodels ecosystem, bridging symbolic model specifications with numerical linear algebra operations via numpy and pandas. The architecture relies on a custom formula parser, a stateful transform protocol for handling splines and categorical coding, and a DesignMatrix ndarray subclass that preserves metadata while maintaining performance. While the codebase demonstrates high craftsmanship in parsing and matrix construction, it is no longer under active feature development, with users recommended to migrate to the successor library 'Formulaic'.