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BARRACUDA

barracuda is the reusable scientific API for BARRACUDA: Bayesian Analysis Resolving Randomness and Alternative Causes Underlying Differential Activity. It supports event-count and ordered-trajectory simulation, PyMC inference, donor-aware hierarchies, marginal-likelihood model comparison, validation, cumulative Bayes-factor scans, diagnostics, plotting, and reproducible export.

Alpha research software

Version 0.2 is an alpha API accompanying a manuscript in preparation. Pin exact versions and retain inputs, priors, settings, seeds, raw posterior draws, and evidence-direction metadata for every reported analysis.

Choose a workflow

Goal Start here
Simulate or fit event counts Event-count guide
Separate donor and cellular variation Donor-aware guide
Fit ordered contact-kill histories Trajectory guide
Test parameter recovery Scientific validation
Measure evidence versus sample size Bayes-factor scans
Check or visualize results Plotting and diagnostics
Look up a function API reference

Install

python -m pip install cyto-barracuda

BARRACUDA currently supports Python 3.12. Inference uses PyMC Sequential Monte Carlo (SMC); runtime and memory can increase steeply with particles, chains, cells, quadrature nodes, fitted models, scenarios, and replicates.

Core principles

  1. Validate first. Public validators return canonical copies and reject ambiguous or unsafe inputs before an expensive fit starts.
  2. Keep evidence directed. A positive log_BF_A_vs_B supports A, the numerator named in the column.
  3. Retain posterior draws. A plot or rounded table cannot reproduce a fit.
  4. Separate exploration from reporting. Smoke settings are useful for code checks, not scientific conclusions.
  5. Treat privacy as a data-governance responsibility. Local execution does not make sensitive data suitable for publication or CI logs.

GitHub Pages activation

The repository includes an artifact-based Pages workflow. A repository administrator must select Settings → Pages → Build and deployment → Source: GitHub Actions before the first deployment. Pull requests and pushes build the site strictly; only pushes to pypackage deploy it. Repository visibility and organization policy determine who can view the published site.

Continue with Getting started, or read the canonical package README.