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Changelog

All notable changes to the quivers library are documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.1.0] - 2026-03-26

Added

Core Categorical Algebra

  • Fundamental category types and morphisms
  • Object declarations and morphism composition
  • Support for latent and observed morphisms
  • Basic categorical operations and abstractions

Stochastic Morphisms

  • Stochastic morphism declarations and semantics
  • Integration with probability theory
  • Support for morphism composition in stochastic settings

Continuous Distributions (30+ Families)

  • Normal distribution and variants (LogitNormal, TruncatedNormal)
  • Beta, Dirichlet for probability simplices
  • Exponential family: Exponential, Gamma, Chi2
  • Heavy-tailed: Cauchy, StudentT, Pareto
  • Bounded: Uniform, Kumaraswamy
  • Half-variants: HalfCauchy, HalfNormal
  • Transformed: LogNormal, Gumbel, Laplace, Weibull
  • Multivariate: MultivariateNormal, LowRankMVN, Wishart
  • Bernoulli variants: Bernoulli, ContinuousBernoulli, RelaxedBernoulli
  • Advanced: RelaxedOneHotCategorical, FisherSnedecor
  • Normalized flows: Flow
  • Categorical and discrete approximations

Monadic Programs

  • Draw statements for sampling from morphisms
  • Observe statements for conditioning and likelihood
  • Return statements with optional labeled outputs
  • Variable binding and destructuring in patterns
  • Program parameters and composition

QVR DSL

  • Complete lexer with token recognition for all language constructs
  • Recursive descent parser with full grammar support
  • Abstract syntax tree (AST) node definitions
  • Program block execution with proper scoping
  • Let bindings for expression computation
  • Built-in let functions: sigmoid, exp, log, abs, softplus
  • Comment support (#)
  • Type expressions: products (*), coproducts (+)
  • Expression operators: composition (>>), tensor product (@), marginalization
  • Indentation-aware program body parsing
  • Specialized handling for draw/observe arguments

Variational Inference Layer

  • Inference interface for probabilistic programs
  • Support for approximate posterior computation
  • Integration with continuous distribution families