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Changelog

Source: NEWS.md

theoryforge 0.6.0

  • New tf_implications() derives the testable implications of a theory’s causal subgraph. It reads the causal propositions as a directed graph, checks that the graph is acyclic, and returns the basis set of implied conditional independencies: one claim per pair of constructs with no causal relation between them, conditioned on the parents of both, in the notation dagitty prints. That set is the shortest complete statement of what a causal theory forbids in data, so it is what a study can be designed to refute. The package cited the derivability of those implications in its own checklist and derived none of them. A cyclic graph has no basis set and is refused with the cycle named, which is what happens to the bundled panic-network example and its amended version. A theory with no causal relations comes back with an empty set and no error. The Python twin gains theory.implications(), returning the same records in the same order. The derived sets were checked against dagitty and ggm, which sit in Suggests for that purpose and whose tests skip when they are absent.

  • A fourth example theory ships with the package, modality-switching.theory.yaml, and it is the worked example for tf_implications(). Both panic-network fixtures are cyclic, so until now every bundled theory showed only what the function refuses. This one states the modality-switching effect in grounded conceptual processing: sensorimotor experience with a concept drives activation of the modality-specific perceptual system, which raises the cost of switching modality between consecutive trials and eases conceptual access, as lexical familiarity with the word form does too. Five constructs and four causal propositions give an acyclic graph with a fork and a collider in it, and a basis set of six conditional independencies, confirmed against dagitty and ggm. The panic fixtures stay as they are: a feedback loop is legitimate theory, and the refusal is worth seeing as well, so the Developing and testing article now shows both outcomes.

  • New tf_example_names() and tf_example_path() reach the theories and the literature corpus bundled with the package, mirroring example_names() and example_path() in the Python twin, so the README quick start runs straight after remotes::install_github() with no clone.

  • tf_validate() refuses an unrecognised top-level field. A misspelt collection key such as predicitions: was dropped without a word, taking its whole collection with it and moving the aggregate score and the gate. The schema’s additionalProperties was set to match, so a third-party validator agrees.

  • Four further refusals replace a silently wrong answer. tf_read() and tf_read_corpus() no longer accept a top-level YAML sequence of mappings, a shape that used to read as a document with every collection empty. tf_simulate() refuses duplicate construct ids, which produced two different but equally plausible trajectories from one file. tf_check() refuses a non-numeric prediction severity, where it used to coerce one, and tf_validate(full = TRUE) reports the same file as invalid, so the scorer and the validator agree about it. tf_embedding_redundancy() refuses a pair of unequal-length vectors, naming the constructs and the lengths, where it used to recycle the shorter one.

  • An enum field written as a YAML sequence, such as theory_form: [network], is now refused. %in% unboxed the one-element list, so the file validated in R and was refused in Python.

  • tf_check() and tf_dossier() record checklist_version, the version of the checklist whose weights and thresholds produced every number in the report, so two reports written against different checklist revisions are no longer silently comparable. tf_simulate() echoes back k, damping and init alongside dt and steps, so a recorded trajectory can be reproduced from what the record itself reports.

  • The causal-testability criterion now describes what it computes. It asserted acyclicity and never checked it. The criterion and the methodology article now state that the export is emitted as written, that it is not verified acyclic, and that the shipped panic-network example is in fact cyclic. No score, gate or status changed. The check the criterion once implied now lives in tf_implications(), and it can refuse a graph outright instead of quietly rescoring it.

  • Every file the package writes goes through one LF-only, UTF-8 writer, so the R half no longer emits CRLF where the Python half emits LF. tf_write() forces UTF-8 as its sibling writers already did, and a failed quarto render no longer returns its output path as though it had succeeded.

  • The network adapters carry the same 30-second timeout as their Python counterparts, and both languages reject a per_page outside OpenAlex’s documented 1-200 range before making a request.

  • Every vignette now turns console colour off and fixes the console width while it renders. pkgdown passes the calling terminal’s colour support into its build subprocess, and the Get started vignette’s failure path therefore published the tf_validate() error with its bold and yellow escape sequences showing as literal text around the words Error and the exclamation mark.

  • inst/WORDLIST is read at last: spelling joins Suggests and a tests/spelling.R runs the check under R CMD check.

theoryforge 0.5.0

  • The development_roadmap view is rebuilt around a theory hub carrying the title, the aggregate score and the gate. Items are ordered blockers first and then by weight, each labelled with its ordinal, the checklist criterion and whether it blocks the gate, with visible edges down the blockers and the advisory items set three abreast.
  • The three SVG chart views (venn, rigour, severity) now declare a width and a height alongside their viewBox, so each renders at its natural size wherever it is embedded. Without an intrinsic size a chart was stretched to the width of its container, and since the three views have different natural widths the same declared 13px label came out at a different size in each one.
  • The venn discs take the construct-border teal for their outline in place of the former navy, which fell below the 3:1 contrast floor for graphical objects on a dark page and left the figure close to invisible under the dark theme.
  • The bundled panic-network fixtures give the three constructs distinct boundary conditions, so the venn view drawn from them shows where construct scopes diverge, where it used to put a zero in six of its seven regions.
  • All of the above are mirrored byte for byte in the Python twin.
  • Documentation: the Get started vignette shows what tf_validate() returns and demonstrates the failure path, and the development article runs tf_osf_push() in its default dry-run mode, where it was previously withheld.

theoryforge 0.4.0

  • The DOT diagram views are redesigned for content and legibility. Every view opens with a shared Meridian style prelude (Helvetica type, role-coloured rounded nodes); labels wrap so nodes stay narrow; workflow and pipeline nodes carry the id together with the relation or type, where a bare word stood; the development roadmap stacks its items in a single column; and the theme landscape colours themes by status. Every view fits a documentation column. The intermediate representation stays byte-identical to the Python twin’s.

theoryforge 0.3.0

  • New tf_render_diagram() renders the digraph views without leaving R: a DiagrammeR widget for the viewer and R Markdown, or a standalone SVG string with as = "svg". It accepts a theory or a raw DOT string, so tf_lit_diagram() output renders the same way; the three SVG chart views pass through unchanged, and causal_dag is refused with a pointer to dagitty. The rendering packages (DiagrammeR, DiagrammeRsvg, htmltools) are in Suggests, so the deterministic core stays dependency-free, and rendering sits outside the cross-language parity contract. The articles now show each digraph rendered beneath its intermediate representation.

theoryforge 0.2.0

  • The severity chart is re-laid out: bars start just past the longest row label and each value trails its own bar. The diagram intermediate representation for tf_diagram(type = "severity") changes accordingly, and it stays byte-identical to the Python twin’s.
  • Documentation: the articles now show the provenance, development_roadmap, pipeline and co_citation views, the embedding-redundancy screen, tf_validate(full = TRUE) and the remaining build verbs, and a new section covers rendering and depositing.

theoryforge 0.1.0

First public release. The package provides a reproducible workflow for building, developing and testing scientific theories, with behaviour pinned by a shared specification (API_SPEC.md) so the R and Python twins return identical verdicts and byte-identical diagram intermediate representations.

  • Core: theory-object input, output and structural validation; a 12-item rigour checklist with a weighted aggregate score and a blocker gate; diagram intermediate representations (nomological net, provenance, causal DAG); and a deterministic lexical construct-redundancy screen. Where the schema expects an array of strings, a nonempty scalar string is read as a singleton list (API_SPEC.md section 4), so natural YAML such as derives_from: p1 yields the same rigour verdict and gate as the Python twin; an empty or whitespace-only scalar counts as absent.
  • Workflow modes: a builder API with auto-logged provenance (BUILDING); an operationalised severity rubric and preregistration export (TESTING); and a Lakatosian progressive-versus-degenerating amendment appraisal (DEVELOPMENT).
  • Literature layer: a deterministic bibliometric mapping (tf_litmap, tf_landscape, tf_lit_diagram), a parity-exempt OpenAlex corpus adapter, and a deterministic, dependency-free check for DOIs not yet cited by a theory (tf_new_evidence_dois), for use with a search from any source, including the companion scopusflow package. tf_lit_diagram() lists the valid types in its unknown-type error, matching tf_diagram().
  • Testing and review: lavaan model-syntax compilation (tf_compile_sem) and a reviewer-facing audit dossier (tf_dossier).
  • Simulation, reporting and deposit: a deterministic dynamical-system runner (tf_simulate), a Quarto report wrapper (tf_render_report), an opt-in embedding redundancy screen (tf_embedding_redundancy), and an OSF deposit adapter (tf_osf_push, dry-run by default). tf_osf_push() percent-encodes the filename component of the upload URL, keeping the dry-run request identical to the Python twin’s.
  • Cross-language determinism: the literature layer and the amendment appraisal sort with radix (codepoint) ordering regardless of locale, matching the Python twin for mixed-case keywords and ids.
  • Metadata: citation("theoryforge") and the About article read the package version from the package metadata.