theoryforge 0.1.0

Systematic theory development.

theoryforge (Python)

Systematic theory development: a rigorous, reproducible workflow for building, developing and testing scientific theories. This is the Python twin of the R package of the same name, and the two return identical results.

The rendered documentation site, with the API reference and worked guides, is at https://pablobernabeu.github.io/theoryforge/python/.

Prefer to click rather than install? The interactive web app runs this package in your browser via Pyodide: load a theory, run any operation, and export the visualisation (SVG/PNG) and the Python code to reproduce it.

import theoryforge as tf

# read + check an existing theory
t = tf.read("../fixtures/panic-network.theory.yaml")
t.validate()                       # structural validation against the shared schema
print(t.report("json"))            # 12-item rigour checklist + gate
print(t.diagram("nomological_net"))# Graphviz DOT
t.redundancy_check()               # lexical jingle-jangle screen

# BUILD a theory programmatically (provenance auto-logged)
b = (tf.new_theory("demo", "Demo theory")
       .add_construct("a", "Alpha", "the first thing", measurement=["m1"], boundary_conditions=["adults"])
       .add_proposition("p1", "a", "b", "increases", mechanism="a drives b")
       .add_prediction("pred1", "a point claim", "point", derives_from=["p1"]))

# DEVELOP: progressive vs degenerating appraisal of an amendment
v1 = tf.read("../fixtures/panic-network.theory.yaml")
v2 = tf.read("../fixtures/panic-network-2026-v2.theory.yaml")
print(v2.appraise_amendment(v1))   # -> {'verdict': 'progressive', ...}

# TEST: operationalised severity + a preregistration document
t.severity()                       # per-prediction risk + computed severity
print(t.preregister())             # markdown prereg

# LITERATURE: map the field, then position the theory against it
corpus = tf.read_corpus("../fixtures/panic-corpus.yaml")
tf.litmap(corpus)                  # keyword co-occurrence, themes, co-citation
t.landscape(corpus)                # -> themes flagged 'under_theorised' / 'crowded' (redundancy risk)
# tf.fetch_corpus("panic disorder theory")  # optional OpenAlex fetch (network call)

The ../fixtures/*.yaml files referenced above are sample theories that live in the project repository; adjust the paths to your own theory files when running the examples.

Install

pip install theoryforge

To work on the package itself, install an editable checkout with the development extras:

pip install -e ".[dev]"

Test

pytest

What the package provides

The deterministic core covers theory-object I/O and validation, the 12-item rigour checklist with its weighted aggregate score and blocker gate, ten diagram exporters and a lexical redundancy screen. The three workflow modes sit on the same object: a BUILDING builder API with auto-logged provenance, the Lakatosian amendment appraisal (DEVELOPMENT), and the operationalised severity rubric with preregistration export (TESTING). The literature layer comprises read_corpus, litmap (keyword co-occurrence, deterministic connected-component themes and co-citation), landscape (which maps a theory and its alternatives onto the themes, flagging under-theorised fronts and redundancy risk), lit_diagram, the network-dependent fetch_corpus OpenAlex adapter, and new_evidence_dois (a deterministic check for candidate DOIs, from any search tool, not yet cited by a theory). compile_sem translates constructs and propositions to lavaan model syntax, and dossier assembles a reviewer-facing audit bundle. Simulation and adapters round the package out: simulate (a deterministic dynamical-system runner over the construct network), render_report (a Quarto report wrapping the dossier), embedding_redundancy (an opt-in, embedder-dependent screen) and osf_push (an OSF deposit adapter, dry-run by default).