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Standard Library Reference

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This is the complete Eta standard library. Every module listed here ships with the distribution and is importable with (import <name>).

Foundations

ModuleImportDescription
std.core(import std.core)Core combinators, list accessors, and a platform predicate.
std.math(import std.math)Mathematical constants and common numeric helpers.
std.collections(import std.collections)Higher-order operations on lists and vectors.
std.io(import std.io)I/O conveniences and dynamic port redirection.
std.atom(import std.atom)Atomic mutable references with compare-and-set semantics.

Strings, Regex & Data Formats

ModuleImportDescription
std.regex(import std.regex)Regular-expression helpers and match-payload accessors.
std.csv(import std.csv)CSV reader and writer.
std.json(import std.json)JSON read/write.

Collections & Data Structures

ModuleImportDescription
std.hashmap(import std.hashmap)Helpers built over the runtime hash-map primitives.
std.hashset(import std.hashset)Helpers built over the runtime hash-set primitives.
std.fact_table(import std.fact_table)Columnar fact tables with indexes, grouping, and aggregation.

Logic & Constraint Programming

ModuleImportDescription
std.logic(import std.logic)Prolog/miniKanren-style goal combinators and solvers.
std.freeze(import std.freeze)Attributed-variable combinators (freeze, dif).
std.clp(import std.clp)CLP over integers and finite domains with labelling.
std.clpr(import std.clpr)CLP over real intervals with linear and quadratic optimisation.
std.clpb(import std.clpb)CLP(B) Boolean propagation solver.
std.db(import std.db)Datalog/Prolog-style relations layered on fact tables.

Numerics & Machine Learning

ModuleImportDescription
std.stats(import std.stats)Descriptive statistics, t-tests, and OLS regression.
std.aad(import std.aad)Tape-based reverse-mode automatic differentiation.
std.torch(import std.torch)libtorch tensors, modules, optimizers, and device management. (requires -DETA_BUILD_TORCH=ON)

Causal Inference

ModuleImportDescription
std.causal(import std.causal)DAG queries, do-calculus, back-door identification, and effect estimation.
std.causal.adjustment(import std.causal.adjustment)GAC, front-door criterion, IV adjustment.
std.causal.identify(import std.causal.identify)ID and IDC algorithms over ADMGs.
std.causal.estimate(import std.causal.estimate)Modern ATE estimators (IPW, AIPW, DML).
std.causal.learn(import std.causal.learn)Structure learning from data.
std.causal.counterfactual(import std.causal.counterfactual)Counterfactual queries under SCMs.
std.causal.mediation(import std.causal.mediation)Natural direct and indirect effects.
std.causal.transport(import std.causal.transport)Transportability of causal effects.
std.causal.admg(import std.causal.admg)ADMG (acyclic directed mixed graph) operations.
std.causal.render(import std.causal.render)Graph and DAG rendering helpers.

OS, Filesystem & Processes

ModuleImportDescription
std.os(import std.os)Environment variables, working directory.
std.fs(import std.fs)Filesystem and path primitives.
std.process(import std.process)Subprocess spawn, wait, and I/O.
std.args(import std.args)Argparse-style command-line parser.

Concurrency & Actors

ModuleImportDescription
std.net(import std.net)High-level networking patterns over NNG. (requires -DETA_BUILD_NNG=ON)
std.supervisor(import std.supervisor)Erlang-inspired actor supervision strategies. (requires -DETA_BUILD_NNG=ON)

Tooling & Development

ModuleImportDescription
std.log(import std.log)Structured logger and sink construction.
std.test(import std.test)Unit-testing framework with assertions and TAP/JUnit reporters.
std.prof(import std.prof)Runtime profiling helpers.
std.time(import std.time)Clocks, sleep, and ISO-8601 formatting.
std.jupyter(import std.jupyter)Notebook cell rendering helpers. (requires eta_jupyter)

See also