stochpylib

Probability · Distributions · Monte Carlo — one coherent Python library, engineered to prove that a complete stochastic-computing stack can live in a single well-tested package.

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stochpylib is not a wrapper around existing statistical libraries — every distribution and algorithm is implemented from scratch on top of scipy.special/optimize/integrate as raw numerical building blocks, with scipy.stats, statsmodels and lifelines used only as independent test oracles. One contract holds everywhere: every distribution exposes .pdf()/.cdf()/.ppf()/.rvs()/.mean()/.var()/.skewness()/.kurtosis()/.entropy()/.mgf()/.cf()/ .fit()/.ks_test(), every stochastic method takes random_state=, and every Monte Carlo estimator returns a shared result object with a point estimate, standard error and confidence interval.

Twenty-three modules are built on that contract, covering the full design spec (794/794 public names): probability, 47 distributions, Monte Carlo, time series, Gaussian processes, survival analysis, queueing, copulas, information theory, Lévy processes, financial stochastics, statistics, random matrix theory, advanced MCMC, numerical methods, Bayesian inference, robust statistics, nonparametric methods, optimization, design of experiments, spatial statistics, visualization and utilities (seeding, parallel/GPU/JIT execution, reproducibility, data cleaning, serialization, interop). See Current Status for the per-module breakdown, Architecture for the structural picture, and Known Limitations for what's still rough at the edges.

Known Limitations

• PyPI lags the repository (0.6.4 published vs. 0.20.0 here — no tag pushed since the early modules). Install from source for the current code; see Download.
• utils's pandas/torch/jax/numba/cupy interop is lazy, confined to utils/_backends.py, and never a runtime dependency; cupy is untested on real GPU hardware (CI fakes it). n_jobs=/backend= results are reproducible but not bit-identical to the legacy serial stream (n_jobs=None/backend=None, the defaults, are unaffected). goodness_of_fit p-values aren't corrected for estimated parameters. queueing's unseeded calls no longer use hidden default seeds. Details: stochpylib/utils/README.md.
• viz renders natively to SVG; matplotlib is optional, backing only .to_matplotlib()/.save(). No interactivity, no 3-D, approximate SVG text metrics.
• statistics's two-sample KS p-value uses the classical asymptotic formula, not scipy's finite-sample refinement (no scipy.special equivalent exists).
• Multivariate distributions expose .pdf() (not .pmf()) and omit .mgf()/.cf() — the one sanctioned interface deviation.
• GP expectation propagation is experimental — prefer Laplace or VI.
• advanced_mcmc has no built-in autodiff — finite differences by default; utils.torch_interface/jax_interface supply exact gradients when installed.
• numerical_methods's FEniCS_Interface solves natively by default (FEniCS only for .to_fenics()); its BDF solver is fixed-step, 2-D elements are P1-only.
• The full test suite is slow (tens of minutes locally — GARCH/VARMA/vine-copula convergence tests); CI runs the same suite every push, so slow-but-green is normal.
• bayesian has no autodiff or general-purpose PPL — non-conjugate posteriors use a numerical grid, Laplace, VI, importance sampling or MCMC; MixtureModel/DirichletProcess cover Gaussian/1-D Poisson only; BayesianNetwork is discrete-only.
• robust_statistics's randomized estimators fall back to seeded subsampling above 5000 candidate subsets (MCD/MVE/LTS/RANSAC/etc.); OGK isn't affine equivariant; Sn/Qn estimators aren't sub-quadratic.
• nonparametric's Anderson-Darling/Cramér-von Mises p-values use asymptotic/table approximations, not finite-sample corrections; local regressors are O(n) per query.
• optimization's metaheuristics (CMA-ES/DE/SA) are single-run, with no restart schemes; InteriorPoint needs a feasible start; no autodiff means O(dim²) finite-difference Hessians.
• experimental_design's optimal designs are multi-start point-exchange (local optima only); coverage gaps in some BoxBehnken/GraecoLatin/Plackett_Burman orders; plots are data-only with a viz-backed to_figure().
• spatial_statistics supports box observation windows only; CoKriging is Markov-Model-1 with one secondary variable; cluster point processes fit by minimum contrast, not closed form.

Per-module detail for every bullet above lives in that module's own README (linked in Module Documentation); exact per-name spec status is in development/Implementation-Checklist.md.

Table of Contents

• Known Limitations
• Quickstart
• Download
• Getting Started
• Requirements
• Architecture
• Current Status
• Project Layout
• Module Documentation
• Development Documentation
• Open Source Files
• Test Suite
• CLI Reference
  • spl --help
  • spl --version
  • spl --test
  • spl update
  • spl info
  • spl show
  • spl demo
  • spl cite
• Release Process
• Roadmap

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Quickstart

from stochpylib.probability import bayes_theorem, total_probability

# Classic disease-screening example: 1% prevalence, 99% sensitivity, 5% false-positive rate.
p_positive = total_probability((0.99, 0.01), (0.05, 0.99))
p_disease_given_positive = bayes_theorem(0.01, 0.99, p_positive)
print(round(p_disease_given_positive, 4))  # 0.1667

from stochpylib.distributions import Normal, Weibull
from stochpylib.montecarlo import SobolSequence, AntitheticVariates

d = Normal(0.0, 1.0)
d.pdf(0.0); d.cdf(1.96); d.ppf(0.975); d.rvs(100, random_state=0)

fitted = Weibull.fit(lifetimes)           # maximum likelihood from data
stat, p_value = fitted.ks_test(data)      # goodness of fit

pts = SobolSequence(dim=5).generate(10_000)                    # low-discrepancy points
price = AntitheticVariates(n_simulations=100_000).price_european_call(
    S=100, K=100, T=1, r=0.05, sigma=0.2)                      # option pricing

from stochpylib.gaussian_processes import GPRegression, RBFKernel

gp = GPRegression(kernel=RBFKernel(length_scale=1.0), noise=0.01).fit(X_train, y_train)
mu, sigma = gp.predict(X_test, return_std=True)     # mean + uncertainty

from stochpylib.copulas import CopulaFit

fit = CopulaFit().fit(returns_2d)                   # dependence modeling
simulated = fit.best_.sample(10_000)                # best family by AIC

Download

If you just want to use stochpylib rather than develop on it, no source checkout is needed:

pip install stochpylib
spl --help        # overview of everything the library offers

Note the Known Limitations above: the published PyPI release currently lags this repository. For the current state of the library, install from source instead:

git clone https://github.com/leon1706-lol/Stochpylib.git
cd Stochpylib
pip install -e .

Getting Started

For local development (this repo cloned, a virtual environment active):

pip install -e ".[dev]"     # runtime deps + pytest
pytest tests/ -v            # full test suite must be green before you start changing things
spl --version               # verify your editable install
spl --test                  # embedded self-check (291 checks), no pytest needed

Then implement or improve one module at a time and run the wrap-up procedure described in CONTRIBUTING.md.

Requirements

• Python ≥ 3.10
• NumPy and SciPy (the only runtime dependencies)
• pytest for the development extras (pip install -e ".[dev]")
• No compilers, no GPU, no other system packages — pure Python/NumPy/SciPy by design
• matplotlib is optional — stochpylib.viz renders natively to SVG with no extra dependency; installing matplotlib additionally unlocks Figure.to_matplotlib() and .save('*.png'/'*.pdf')
• pandas/torch/jax/numba/cupy are optional — stochpylib.utils needs none of them; each is lazily imported only when a function that needs it is called (pip install "stochpylib[pandas,torch,jax,numba]", or [gpu] for cupy)

Architecture

One subpackage per module, all built around the shared distribution contract:

• scipy.special/optimize/integrate supply raw numerics only.
• probability/distributions build the exact primitives and the common interface.
• Every higher-level module consumes those primitives through the same conventions: random_state= seeds, fluent .fit(), shared result objects (MCResult/ ForecastResult/QueueResult).
• The test suite treats scipy.stats/statsmodels/lifelines as independent oracles — library code never wraps them.

System Flow

flowchart LR
    A["numpy / scipy.special / scipy.optimize / scipy.integrate<br/>(raw numerical building blocks only)"] --> B["stochpylib.probability<br/>sample spaces, Bayes, exact combinatorics"]
    B --> C["stochpylib.distributions<br/>47 distributions behind one interface"]
    C --> D["stochpylib.montecarlo<br/>QMC sequences, estimators, variance reduction"]
    C --> E["stochpylib.timeseries<br/>ARIMA/GARCH, filters, changepoints, spectral"]
    C --> F["stochpylib.gaussian_processes<br/>composable kernels, exact/sparse inference"]
    C --> G["stochpylib.copulas<br/>elliptical/Archimedean/vines"]
    C --> H["stochpylib.survival<br/>KM, Cox, competing risks"]
    B --> I["stochpylib.queueing<br/>closed forms + discrete-event simulation"]
    C --> J["stochpylib.information_theory<br/>entropy, divergences, channels, coding"]
    C --> M["stochpylib.levy_processes<br/>jump-diffusion pricing, subordinators, SDE solvers"]
    C --> N["stochpylib.financial_stochastics<br/>option pricing, Heston/SABR, rate models, risk, credit, portfolio"]
    B --> O["stochpylib.statistics<br/>descriptive · estimation · hypothesis tests · regression · multivariate"]
    C --> P["stochpylib.random_matrix<br/>ensembles, limit laws, Haar rotations, spectral statistics"]
    C --> Q["stochpylib.advanced_mcmc<br/>MCMC samplers, HMC/NUTS, SMC, diagnostics, variational inference"]
    A --> R["stochpylib.numerical_methods<br/>quadrature, ODE/SDE solvers, linear algebra, roots, interpolation, PDE"]
    Q --> S["stochpylib.bayesian<br/>conjugate/grid/Laplace/VI/IS posteriors, Bayesian models, model selection"]
    O --> T["stochpylib.robust_statistics<br/>robust location/scale, high-breakdown regression, MCD/MVE/OGK, bootstraps"]
    C --> U["stochpylib.nonparametric<br/>density estimation, resampling/rank tests, dependence measures, local regression"]
    A --> V["stochpylib.optimization<br/>gradient & quasi-Newton methods, metaheuristics, stochastic approximation, constrained solvers"]
    D --> W["stochpylib.experimental_design<br/>classical, optimal & space-filling designs, response surfaces, sensitivity analysis"]
    F --> X["stochpylib.spatial_statistics<br/>variograms, kriging, random fields, point processes, spatial autocorrelation"]
    C --> Y["stochpylib.viz<br/>SVG-native statistical plots, matplotlib optional"]
    A --> Z["stochpylib.utils<br/>seeds/streams, parallel/GPU/JIT backends, reproducibility, data, io, compat"]
    D --> K["shared result objects<br/>MCResult / ForecastResult / QueueResult"]
    E --> K
    F --> K
    K --> L["tests/<br/>scipy.stats + statsmodels + lifelines as independent oracles"]
    I --> L
    M --> L
    N --> L
    O --> L
    P --> L
    Q --> L
    R --> L
    S --> L
    T --> L
    U --> L
    V --> L
    W --> L
    X --> L
    Y --> L
    Z --> L

Tech Stack

flowchart TB
    A["Runtime"] --> A1["Python >= 3.10"]
    A --> A2["NumPy"]
    A --> A3["SciPy (special / optimize / integrate only)"]
    A --> A4["matplotlib (optional, lazy -- viz raster/PDF backend only)"]
    A --> A5["pandas/torch/jax/numba/cupy (optional, lazy -- utils/_backends.py only)"]
    B["Packaging"] --> B1["setuptools + pyproject.toml"]
    B --> B2["PyPI via Trusted Publisher (OIDC)"]
    B --> B3["spl console CLI (cli.py)"]
    C["Testing"] --> C1["pytest (tests/, outside the package)"]
    C --> C2["scipy.stats / statsmodels / lifelines as test oracles"]
    C --> C3["spl --test embedded self-check (291 checks)"]
    D["CI / release"] --> D1["GitHub Actions: ci.yml, publish.yml, release.yml"]
    E["Design vault"] --> E1["Stochpylib-Obsidian-Vault (private, generated code graph)"]

These two diagrams are the high-level summary. For the full system — the objective, the module map, the common distribution contract, and the cross-cutting conventions every module must follow — see development/architecture.md.

Current Status

Twenty-three modules implemented and tested — 794 / 794 spec names:

Module	Public names	What's inside
stochpylib.probability	21	Sample spaces, Bayes' theorem, exact combinatorics, independence checks
stochpylib.distributions	60	47 distributions (discrete/continuous/multivariate/heavy-tailed)
stochpylib.montecarlo	25	QMC sequences, MC estimators, variance reduction, applications
stochpylib.timeseries	61	ARIMA/GARCH families, Kalman/particle filters, HMMs, spectral analysis
stochpylib.gaussian_processes	36	Kernel zoo, exact/sparse/approximate-classification GPs, DeepGP
stochpylib.copulas	26	Elliptical/Archimedean/empirical copulas, vines, dependence measures
stochpylib.survival	28	Kaplan-Meier, Cox regression, parametric censored fits, competing risks
stochpylib.queueing	29	M/M/1–Jackson networks, blocking formulas, discrete-event simulation
stochpylib.information_theory	31	Entropy, divergences, mutual information, channel capacity, coding
stochpylib.levy_processes	33	Jump-diffusion pricing, subordinators, Hawkes/branching processes, SDE solvers
stochpylib.financial_stochastics	50	Option pricing, stochastic/local vol, rate models, risk, credit, portfolio
stochpylib.statistics	48	Estimation, hypothesis tests, regression, multivariate methods
stochpylib.random_matrix	23	Classical ensembles, limit laws, Haar rotations, spectral statistics
stochpylib.advanced_mcmc	35	MH/Gibbs/HMC/NUTS/SMC samplers, diagnostics, variational inference
stochpylib.numerical_methods	38	Quadrature, ODE/SDE solvers, linear algebra, root finding, PDE tools
stochpylib.bayesian	25	Conjugate/grid/Laplace/VI posteriors, Bayesian models, model selection
stochpylib.robust_statistics	28	Robust location/scale, high-breakdown regression, MCD/MVE/OGK, bootstraps
stochpylib.nonparametric	31	Density estimation, resampling/rank tests, dependence measures, local regression
stochpylib.optimization	32	Gradient/quasi-Newton methods, metaheuristics, stochastic approximation, constrained solvers
stochpylib.experimental_design	29	Classical/optimal/space-filling designs, response surfaces, sensitivity analysis
stochpylib.spatial_statistics	32	Variograms, kriging, random fields, point processes, spatial autocorrelation
stochpylib.viz	35	SVG-native statistical plots, matplotlib optional
stochpylib.utils	38	Seeding, parallel/GPU/JIT backends, reproducibility, data cleaning, interop

Exact progress against the full design spec lives in development/Implementation-Checklist.md (currently 794 / 794 public names).

Project Layout

The repository is a nested installable package, a test suite outside it, development docs, and the private design-spec vault. Every folder carries its own short README.md as an entry-point guide:

Folder	Guide	What lives there
stochpylib/	package guide	the installable package: twenty-three module subpackages, cli.py, selftest.py
tests/	suite guide	per module: an oracle suite (tests.py) and an end-to-end API sweep (e2e.py), plus the cross-module library/docs/cli suites, outside the installed package
development/	dev-docs guide	architecture, infrastructure runbook, build history, bug audit log, progress checklist
.github/	—	CI / PyPI-publish / GitHub-Release workflows, issue & PR templates
Stochpylib-Obsidian-Vault/	—	the full design-spec vault; maintained privately, not part of this repo

Module Documentation

Every module subpackage has its own README with the full detail on what it owns, its conventions and its documented limitations — this table is the index:

Module	What it owns	Docs
stochpylib/probability/	Sample spaces, Bayes, exact-integer combinatorics, independence checks	README
stochpylib/distributions/	47 distributions behind the common distribution contract	README
stochpylib/montecarlo/	Quasi-random sequences, estimators, variance reduction, applications	README
stochpylib/timeseries/	Linear/volatility models, filters, changepoints, spectral analysis, forecasting	README
stochpylib/gaussian_processes/	Composable kernels, exact/sparse regression, classification, hyperparameters	README
stochpylib/copulas/	Elliptical/Archimedean/empirical copulas, vines, dependence measures	README
stochpylib/survival/	Kaplan-Meier, parametric fits, Cox/AFT/FineGray regression, competing risks	README
stochpylib/queueing/	Single queues, birth-death formulas, networks, discrete-event simulation	README
stochpylib/information_theory/	Entropy, divergences, mutual information, channels, coding	README
stochpylib/levy_processes/	Lévy-Khintchine core, jump-diffusion pricing, subordinators, advanced point/branching/field processes, SDE solvers	README
stochpylib/financial_stochastics/	Option pricing & Greeks, stochastic/local vol, short-rate models, risk, credit, portfolio construction	README
stochpylib/statistics/	Descriptive stats, estimation, hypothesis tests, regression, multivariate methods	README
stochpylib/random_matrix/	Classical ensembles, limiting spectral laws, Haar rotations, spectral statistics	README
stochpylib/advanced_mcmc/	MCMC samplers (MH to NUTS/RMHMC), slice/tempering/SMC/particle/transdimensional methods, diagnostics, variational inference	README
stochpylib/numerical_methods/	Gauss quadrature & adaptive/cubature integration, ODE/SDE solvers, native linear algebra, root finding, interpolation, PDE tools	README
stochpylib/bayesian/	Priors/likelihoods/posteriors, conjugate engine, Bayesian models, model selection, posterior approximations	README
stochpylib/robust_statistics/	Robust location/scale, high-breakdown regression, robust covariance, resampling	README
stochpylib/nonparametric/	Density estimation, empirical/likelihood, resampling & rank tests, dependence measures, local regression	README
stochpylib/optimization/	Gradient & quasi-Newton methods, global metaheuristics, stochastic approximation, constrained solvers	README
stochpylib/experimental_design/	Classical, optimal and space-filling designs, response surfaces & surrogates, effect and sensitivity analysis	README
stochpylib/spatial_statistics/	Variograms and fitting, kriging (simple/ordinary/universal/co-/indicator/disjunctive), covariance-driven random fields, point processes, spatial autocorrelation tests	README
stochpylib/viz/	SVG-native statistical plots (matplotlib optional): distribution/process/diagnostic/multivariate/special-purpose plots, all reusing the rest of the library's numbers	README
stochpylib/utils/	Seeds/streams, parallel/GPU/JIT backends, reproducibility, distribution fitting & data cleaning, serialization, numpy/scipy/pandas/torch/jax interop	README

Development Documentation

Document	Contents
development/README.md	Index of this folder
development/architecture.md	The full system architecture: objective, diagrams, module map, the common distribution contract, cross-cutting conventions, package layout rules
development/infrastructure.md	Build/packaging/CI runbook: local setup, the spl CLI, GitHub Actions workflows, the PyPI release pipeline
development/project_structure.md	The annotated directory tree of the repository
development/Development.md	Layout decisions & workflow notes
development/CHANGELOG.md	Append-only log, one entry per build phase
development/Probleme.md	Bug audit log in Problem → Fix → Verification format with a status legend
development/Implementation-Checklist.md	Every planned public name as a checkbox
todo.md	Owner's planning canvas — the current objective(s) and personal notes for the next AI agent to pick up; not a generated backlog, expect it to be rewritten as priorities change

Open Source Files

File	Purpose
LICENSE	MIT
CONTRIBUTING.md	Dev setup, ground rules, semver & deprecation policy, PR checklist
CODE_OF_CONDUCT.md	Contributor Covenant 2.1
SECURITY.md	Private vulnerability reporting (72 h acknowledgment)
AGENTS.md	Agent/contributor guide: project conventions, verification workflow, file sync checklist

Test Suite

pytest tests/ -v

2937 passed / 2 skipped (the 2 skips are documented VonMises/Kumaraswamy scipy cross-checks, covered by dedicated checks instead).

• Deterministic — fixed seeds everywhere, lives outside the installed package.
• Oracles: scipy.stats, statsmodels, brute-force references; assertions set at ≥ 3 SE.
• tests/docs/ keeps every number on this page in sync with reality — fails on any drift.
• tests/cli/ covers the spl surface with mocked PyPI responses; no test touches the network.
• Every module carries an end-to-end API sweep (tests/<module>/e2e.py) — one exercise per public name, guarded so a new name can't ship without one.
• CI runs one visible smoke (<module>) job per module (oracle suite + sweep + spl demo).
• spl --test re-verifies any installation in seconds, no pytest needed.

CLI Reference

Every install (PyPI wheel or pip install -e .) registers one console command, spl:

spl --help

• Full inventory of the installed library: modules (with public name counts), all public functions per module, every distribution class.
• Generated dynamically from the package's __all__, so it never goes stale.
• Also shows the common distribution interface and a runnable quick-start snippet.
• Bare spl shows the same thing.

spl --version

$ spl --version
0.20.0
latest on PyPI: 0.6.4  (installed version is newer / unreleased)

• Prints the installed version (pip metadata, falling back to the in-code version).
• Compares against the latest version published on PyPI and states the relationship (update available / up to date / installed is newer).
• Non-blocking and offline-safe: 4-second timeout, 24h cache, a clear "PyPI check unavailable" message when offline.
• Disable entirely with STOCHPYLIB_SKIP_UPDATE_CHECK=1.

spl --version --list

$ spl --version --list
0.20.0
latest on PyPI: 0.6.4  (installed version is newer / unreleased)
3 published versions:
  0.1.0
  0.1.1
  0.6.4         latest

• Lists every version ever published on PyPI, in release order.
• Installed version marked * installed, newest marked latest.
• Always fetches fresh metadata (never served from the 24h cache).

spl --test

• The embedded self-check suite shipped inside the wheel (291 checks): package sanity, per-module spec conformance, one closed-form spot check per distribution family, Monte Carlo convergence sanity, cross-module workflows, offline CLI-helper logic.
• Works after any pip install — no pytest, no source checkout — the quickest way to verify an installation.
• Exits non-zero on any failure.

spl update

spl update [--vers VERSION] [--yes] [--dry-run] [--force]

• Switches the installed PyPI package to any published version — upgrade, downgrade, or pin (spl update --vers 0.6.1); without --vers it updates to the latest release.
• Validates the target against PyPI's actual release list and refuses unknown versions (printing the most recent published ones).
• Detects editable/source installs (spl update manages the pip package, not a source checkout) and refuses them without --force.
• Prints the exact plan — installed, target, and the precise python -m pip install stochpylib==X.Y.Z command — then asks for confirmation unless --yes is given.
• --dry-run prints the plan and executes nothing.

spl info

• Environment report: stochpylib version, install mode (editable/wheel/source).
• Python and platform, NumPy/SciPy versions.
• Module inventory with per-module public-name counts.
• The quickest answer to "what exactly is installed here?"

spl show

spl show <Name>

• Prints the qualified path, constructor signature and docstring of any public name — spl show Normal, spl show GARCH, spl show bayes_theorem.
• Searches every implemented module's exports.
• Unknown names get did you mean: suggestions from close matches and a non-zero exit.

spl demo

spl demo [module]

A live mini-example for one implemented module against the real installation — deterministic, fixed seeds, a few seconds each:

• probability — Bayes screening problem
• distributions — distribution fit + KS test
• montecarlo — Sobol sequence + option pricing vs. Black-Scholes
• timeseries — AR fit + forecast
• gaussian_processes — GP regression with uncertainty
• copulas — copula AIC selection
• survival — Kaplan-Meier
• queueing — M/M/1 closed form
• information_theory — entropy + Huffman coding
• levy_processes — Kou jump-diffusion pricing + a tempered-stable subordinator path
• financial_stochastics — Black-Scholes/Heston pricing + historical VaR
• statistics — descriptive stats + t-test + OLS regression + PCA
• random_matrix — GOE spectrum vs. the semicircle + Marchenko-Pastur + level repulsion
• advanced_mcmc — NUTS on a correlated Gaussian (R-hat/ESS) + slice sampling + SMC evidence
• numerical_methods — Gauss-Legendre quadrature + Dormand-Prince energy conservation + Brent implied vol + Crank-Nicolson PDE price + a CTMC transition-matrix exponential
• bayesian — conjugate coin-flip posterior + Bayesian linear regression (WAIC) + a Bayes factor + a Bayesian-network query
• robust_statistics — contaminated-sample comparison + Theil-Sen/MM vs. OLS under outliers + MCD outlier flags + a block-bootstrap SE
• nonparametric — bimodal KDE fit + Kruskal-Wallis + distance correlation + local-linear R² + an isotonic-regression check
• optimization — BFGS on Rosenbrock + particle swarm/CMA-ES on Rastrigin-5 + Levenberg-Marquardt + an augmented-Lagrangian solve
• experimental_design — a 2^(5-1) fraction's alias structure + Lenth's method + a D-optimal design + a CCD stationary point + Ishigami Sobol indices + a maximin LHS
• spatial_statistics — a fitted variogram + ordinary kriging + Moran's I + a Ripley-K CSR verdict + a Thomas cluster process
• viz — a QQ-plot + AR(1) ACF + Kaplan-Meier median (+ matplotlib availability check)
• utils — set_seed() reproducibility + AIC-ranked fit + parallel pi estimate + a benchmark timing + backend availability report

Bare spl demo lists the available demos.

spl cite

• Plain-text citation plus a ready-to-paste BibTeX entry.
• Versioned with the installed release.

Release Process

Releases are fully automated from tags:

1. Update the version in pyproject.toml and stochpylib/__init__.py (semver — see the policy in CONTRIBUTING.md)
2. Tag and push:

   git tag vX.Y.Z && git push origin vX.Y.Z
   

3. CI runs the full test matrix, builds sdist + wheel, smoke-verifies the wheel (spl --version, spl --test) and publishes to PyPI via Trusted Publisher (OIDC — no API tokens stored anywhere); a second workflow creates the matching GitHub Release with auto-generated changelog notes

Prerequisite for step 3: configure the Trusted Publisher once under pypi.org → your project → Publishing.

Roadmap

All 23 modules of the design spec are implemented (794/794 public names). Ongoing work:

• Close the documented Known Limitations above.
• Whatever todo.md sets as the next objective.

Full history: development/CHANGELOG.md.

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Built by Leon Schwarzkopf, leonschwarzkopf08@gmail.com

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