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.
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.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.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.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.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.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.tf_diagram(type = "severity") changes
accordingly, and it stays byte-identical to the Python twin’s.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.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.
derives_from: p1 yields the same rigour verdict and
gate as the Python twin; an empty or whitespace-only scalar counts as
absent.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().tf_compile_sem) and a reviewer-facing audit dossier
(tf_dossier).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.citation("theoryforge") and the About article
read the package version from the package metadata.