Inputs
Choose the package source, import path, and modules that match your app, service, or notebook environment.
SDK code usage docs
See real usage in your language without fighting the page.
These public docs focus on practical code usage: install, graph generation, discrete reasoning, layout, continuous causal workflows, and package manifests. Full licensed API reference is provided with purchased SDK packages.
Install and import
Start with the package and import path that match your stack.
These public pages are code usage documentation for evaluation and adoption. Licensed SDK packages include the deeper API reference after purchase.
Outputs
You get the concrete constructors and helper functions used by the later samples, so installation is tied directly to usage.
Package / module
darkstar-python
Python
Install the licensed Python SDK from your artifact feed and initialize it before using reasoning logic.
Discrete BBN graph generation
Generate singly connected and multi-connected graphs.
Use the generator helpers when you want deterministic seed-driven graph creation for demos, tests, and benchmarking before you hand-tune a production model.
Inputs
Pass graph-size controls such as node count, edge density, state count, and a seed so the generated shape is reproducible.
Outputs
The generator returns a graph plus starter parameters that can flow directly into model construction and benchmark fixtures.
Package / module
darkstar.generator
Python
Discrete BBN
Build a small discrete model from a graph and CPTs.
Every language now starts from explicit graph structure and CPT definitions so the first model is created in code instead of being waved in from a file.
Inputs
Provide a directed graph and one CPT per node, including root distributions and any parent-conditioned rows.
Outputs
The constructor returns a reasoning model that can answer exact associational, interventional, and counterfactual queries.
Package / module
darkstar.discrete
Python
Discrete BBN evidence
Run probability queries with hard, soft-finding, and virtual evidence.
Darkstar uses pquery as the short alias for a probability query: it returns posterior distributions after conditioning on evidence. Start with an observed state, then move to weighted finding and virtual evidence when you want softer beliefs.
Inputs
Choose the queried nodes, then provide either a hard observed value, a weighted finding over the node states, or a virtual evidence vector over the same states.
Outputs
Each pquery call returns the posterior distribution for the requested nodes, making it easy to compare how different evidence styles shift the result.
Package / module
darkstar.discrete
Python
Use the discrete model from the build example above; pquery accepts hard observation maps and weighted evidence potentials built by the helper methods.
Discrete BBN
Run associational, interventional, and counterfactual queries.
The discrete quickstart now covers marginals, joints, conditionals, evidence probabilities, interventions, and counterfactual mirrors instead of stopping at a single posterior call.
Inputs
Choose the nodes or target you want, then add factual evidence, optional given variables, and hypothetical interventions when you move into counterfactual analysis.
Outputs
Associational calls return potentials or scalar evidence probabilities, iquery returns causal effects, and the counterfactual helpers mirror the same marginal, joint, conditional, and evidence forms.
Package / module
darkstar.discrete
Python
Graph layout
Lay out the graph for explorer, editor, and dashboard UIs.
Layout helpers give you deterministic node coordinates for customer-facing graph views without forcing you into one front-end framework.
Inputs
Pass the graph you already built plus a named layout algorithm supported by the wrapper or runtime.
Outputs
You get stable node coordinates that can be fed into canvases, SVGs, editors, and dashboard components.
Package / module
darkstar.layout
Python
Continuous Gaussian SCM
Build a linear-Gaussian SCM in code.
The continuous examples mirror the discrete ones: define the graph, provide ordered parameters, and construct the model in code.
Inputs
Define the graph, the ordered variable list, and the Gaussian parameters such as means and covariance terms expected by the wrapper.
Outputs
You get a continuous reasoning model ready for posterior, intervention, and counterfactual workflows.
Package / module
darkstar.continuous
Python
Continuous Gaussian SCM
Run posterior, interventional, and counterfactual SCM queries.
For continuous SCMs, pquery is the probability-query alias for conditioning on observed numeric values. Use iquery for interventions and cquery for abduction-action-prediction workflows.
Inputs
Use observed numeric values for conditioning, then provide interventions or counterfactual scenarios when you want to compare worlds.
Outputs
The calls return posterior moments, interventional summaries, and counterfactual estimates that you can chart or feed into downstream analysis.
Package / module
darkstar.continuous
Python
Download manifest
Review the exact SDK package contents before you install.
Every SDK archive includes a manifest at the zip root, and the account page provides the same manifest as a separate download for that archive.
Inputs
Choose a language, platform, and SDK version from your account page, then download the archive and its matching manifest.
Outputs
The manifest identifies the release, platform, package descriptor snapshots, included docs, native runtime files, and per-file SHA-256 hashes.
Package / module
Python download manifest
Python
The account page also provides the same manifest as a separate download so you can review file names, sizes, and hashes before installing the archive.