Package index • theoryforge

Core IO

Read, validate and write theory objects.

tf_read(): Read a theory object from a YAML or JSON file
tf_validate(): Validate a theory object
tf_write(): Write a theory object to YAML or JSON

Packaged examples

Example theories shipped inside the package, so that nothing has to be downloaded to follow the documentation. The Python twin reaches the same files through example_names() and example_path().

tf_example_names(): Names of the packaged example files
tf_example_path(): Path to a packaged example file

Builder (BUILDING mode)

Construct a theory incrementally with provenance tracking.

tf_theory(): Start a new, empty theory object (BUILDING mode entry point)
tf_add_construct(): Add a construct to a theory (BUILDING mode)
tf_add_proposition(): Add a proposition to a theory (BUILDING mode)
tf_add_prediction(): Add a prediction to a theory (BUILDING mode)
tf_add_alternative(): Add an alternative theory (BUILDING mode)
tf_add_assumption(): Add an auxiliary assumption (BUILDING mode)
tf_set_formal_model(): Set the formal model (BUILDING mode)

Rigour

Score a theory against the versioned rigour checklist, and report how severely each bounded prediction passed its test.

tf_check(): Compute the rigour checklist report
tf_report(): Render the rigour report as a string
tf_severity(): Per-prediction claim-form riskiness
tf_post_data_severity(): Post-data severity of each bounded prediction

Redundancy

Deterministic lexical redundancy screen.

tf_tokens(): Tokenise a string into a set of content tokens
tf_jaccard(): Jaccard similarity of two token sets
tf_redundancy_check(): Pairwise lexical similarity of construct definitions
tf_embedding_redundancy(): Embedding-based pairwise construct-redundancy screen

Diagram

Byte-identical diagram intermediate representations, and native rendering.

tf_diagram(): Render a diagram intermediate representation
tf_lit_diagram(): Render a literature-layer diagram intermediate representation
tf_render_diagram(): Render a diagram in the viewer or as SVG

Develop

Lakatosian amendment appraisal.

tf_appraise_amendment(): Appraise an amendment as progressive, degenerating, or neutral

Testing and review

The conditional independencies a causal theory commits to, preregistration, SEM compilation and the audit dossier.

tf_implications(): Derive a theory's implied conditional independencies
tf_preregister(): Render the hypotheses section of a preregistration
tf_compile_sem(): Compile a theory to lavaan model syntax
tf_dossier(): Render a theory audit dossier (Markdown)

Simulation

Integrate the construct network as a dynamical system.

tf_simulate(): Simulate a theory's construct network as a linear dynamical system

Reporting and deposit

Render a report and deposit it.

tf_render_report(): Write a Quarto report for a theory
tf_osf_push(): Deposit a theory's audit dossier to OSF storage

Literature layer

Bibliometric mapping of a literature corpus.

tf_read_corpus(): Read a literature corpus from a YAML or JSON file
tf_litmap(): Bibliometric map of a literature corpus (deterministic)
tf_landscape(): Map a theory and its alternatives onto a literature landscape (deterministic)
tf_fetch_corpus(): Build a corpus from the OpenAlex API (network call)
tf_new_evidence_dois(): DOIs not already cited by a theory (deterministic)