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Developing and testing a theory

Source: vignettes/articles/developing-and-testing.Rmd
developing-and-testing.Rmd

The companion vignette covers building a theory and checking its rigour. This article continues the workflow into development and testing. It shows how to operationalise severity, derive what the causal graph forbids in data, export a preregistration, appraise an amendment in Lakatosian terms, compile a measurement model for lavaan and assemble a reviewer-facing audit bundle. The worked example reads the panic-network fixture shipped with the package, joined in one section by the shipped modality-switching fixture, so every chunk runs with only theoryforge loaded.

theory <- tf_read(
  system.file("fixtures/panic-network.theory.yaml", package = "theoryforge")
)
theory$id
[1] "panic-network-2026"

Operationalised severity

A prediction earns evidential weight in proportion to the risk it takes. A point prediction stakes more on the data than a directional one, and that asymmetry should be made explicit before any test is run. tf_severity() reports, for each prediction in file order, the riskiness of its claim form and the resulting computed severity.

tf_severity(theory)
  prediction_id        type risk_score computed_severity
1         pred1       point        0.9               1.0
2         pred2    interval        0.7               0.7
3         pred3 directional        0.4               0.3

The risk_score reflects the prediction type, while computed_severity applies the discounts and bonuses defined in the specification. Reading these values before data collection guards against the temptation to read a weak, hard-to-fail prediction as though it were a stringent test.

What the causal graph commits you to

Severity scores the predictions a theory states. A causal theory also commits itself to claims it never states, because the propositions taken together are a directed graph, and a directed acyclic graph entails a set of conditional independencies. tf_implications() derives them. Every pair of constructs with no causal relation between them yields one claim: the two are independent once the parents of both are held fixed. That collection is the basis set, from which every other independence the graph entails follows, and each member is something a study can be designed to look for and fail to find.

The panic-network example does not get that far. Its third proposition returns from perceived threat to arousal, closing the feedback loop the theory is about, and a graph with a cycle has no basis set at all.

Error:
! implications requires an acyclic causal graph; cycle found: c_arousal -> c_perceived_threat -> c_arousal

The refusal names the cycle it found, so nobody has to hunt for it. Where a theory does hold a feedback loop, this is the point at which the loop has to be modelled over time, since a single graph cannot carry it.

The package ships a second example whose causal graph is acyclic, a theory of modality switching in grounded conceptual processing. Sensorimotor experience with a concept drives activation of the modality-specific perceptual system. That activation both raises the cost of switching modality between consecutive trials and eases conceptual access, which lexical familiarity with the word form eases as well.

switching <- tf_read(
  system.file("fixtures/modality-switching.theory.yaml", package = "theoryforge")
)

implied <- tf_implications(switching)
implied$n_edges
[1] 4
implied$n_implications
[1] 6
writeLines(vapply(implied$implications, function(i) i$statement, character(1)))
c_sensorimotor_experience _||_ c_switch_cost | c_modality_activation
c_sensorimotor_experience _||_ c_conceptual_access | c_modality_activation, c_lexical_familiarity
c_sensorimotor_experience _||_ c_lexical_familiarity
c_modality_activation _||_ c_lexical_familiarity | c_sensorimotor_experience
c_switch_cost _||_ c_conceptual_access | c_modality_activation, c_lexical_familiarity
c_switch_cost _||_ c_lexical_familiarity | c_modality_activation

Five constructs and four causal propositions leave six non-adjacent pairs, and so six claims, each written in the notation dagitty prints. Two of them are worth dwelling on. Switch cost and ease of conceptual access are the two children of a fork at modality activation, so the theory says the correlation between them is entirely due to that shared cause and vanishes once it is held fixed. Sensorimotor experience and lexical familiarity meet only at the collider at conceptual access, and the theory says they are unrelated to each other with nothing held fixed, which is what an account deriving perceptual knowledge from distributional word statistics would deny. Neither claim appears among the theory’s own predictions, and either could sink it.

Preregistration

tf_preregister() renders a preregistration document as a single markdown string. It records the predictions, each with its claim type and derivation, plus the severity values, in a fixed order, so the output is stable across runs and suitable for deposit. Because the string is markdown, it renders below as the document a registry would receive.

prereg <- tf_preregister(theory)
cat(prereg)

Preregistration: Network theory of panic disorder

  • Theory ID: panic-network-2026
  • Schema version: 1.0
  • Maturity: developing
  • Derivation chain verified: yes

Hypotheses

  1. [point] An interoceptive challenge raises arousal to a specified level within 90 seconds. (derives from: p1, p3)
  2. [interval] Avoidance frequency falls within a 20-35% band after exposure therapy. (derives from: p2)
  3. [directional] Higher perceived threat is associated with more avoidance. (derives from: p2)

Severity

  • pred1: severity 1.0, risk 0.9
  • pred2: severity 0.7, risk 0.7
  • pred3: severity 0.3, risk 0.4

Passing a path writes the same markdown to disk with LF line endings, which is convenient when the document is committed to a repository or uploaded to a registry.

prereg_path <- tempfile(fileext = ".md")
invisible(tf_preregister(theory, path = prereg_path))
file.exists(prereg_path)
[1] TRUE

Appraising an amendment

Theories change. The question is whether a change is progressive, in that it predicts and then corroborates something new, or degenerating, in that it only adds assumptions to absorb anomalies. tf_appraise_amendment() compares an amended theory against its prior version and returns a verdict in those terms.

Here the prior is the panic-network fixture and the amended version is its 2026 revision, which adds one new prediction that is later corroborated and introduces no new ad-hoc assumptions.

v2 <- tf_read(
  system.file(
    "fixtures/panic-network-2026-v2.theory.yaml",
    package = "theoryforge"
  )
)

appraisal <- tf_appraise_amendment(v2, theory)
appraisal$verdict
[1] "progressive"
appraisal$new_predictions
[1] "pred4"
appraisal$corroborated_new
[1] "pred4"
appraisal$ad_hoc_assumptions
character(0)

A progressive verdict reflects new content that survived a test. Had the amendment instead added assumptions tied to specific anomalies without generating corroborated predictions, the verdict would record that as degenerating.

Compiling a measurement model

When constructs carry measurement indicators, tf_compile_sem() compiles them into lavaan model syntax. Constructs with indicators become latent measurement models with =~, and propositions become structural paths with ~, covariances with ~~ or annotated moderation lines.

# lavaan model generated by theoryforge for panic-network-2026
# Measurement model
c_arousal =~ heart_rate_variability + self_reported_arousal
c_perceived_threat =~ threat_appraisal_questionnaire
c_avoidance =~ behavioural_avoidance_task + diary_counts
# Structural model
c_perceived_threat ~ c_arousal
c_avoidance ~ c_perceived_threat
c_arousal ~ c_perceived_threat

The function returns a plain string and does not require lavaan to be installed. The output can be passed directly to lavaan::sem() once the data are available.

The reviewer audit bundle

tf_dossier() assembles a single markdown document for a reviewer or editor. It collects the header, the rigour-checklist table, the severity list, the provenance trail and the preregistration into one bundle, so a reader can follow the theory from its claims through to its planned tests in one place. The opening of the bundle, through the rigour-checklist table, renders below.

dossier <- tf_dossier(theory)
cat(strsplit(dossier, "\n## Severity", fixed = TRUE)[[1]][1])

theoryforge dossier: Network theory of panic disorder

  • Theory ID: panic-network-2026
  • Maturity: developing
  • Checklist version: 1.0
  • Aggregate rigour score: 84.8/100
  • Gate: pass
  • Blockers failed: 0

Rigour checklist

item status score weight
falsifiability pass 1.0 0.15
precision pass 0.667 0.1
risk_severity pass 0.567 0.1
parsimony pass 0.667 0.08
non_redundancy pass 0.909 0.1
construct_clarity pass 1.0 0.08
scope pass 1.0 0.06
logical_why pass 1.0 0.08
causal_testability pass 1.0 0.06
diagnosticity pass 0.333 0.06
formalisation pass 1.0 0.05
derivation_chain pass 1.0 0.08

The severity list, the provenance trail and the preregistration follow in the full document, which is deterministic, so the same theory always produces the same dossier.

Rendering and depositing

tf_render_report() writes the dossier as a Quarto source document, ready to render to HTML or PDF. With render = FALSE, which is the default, it writes the .qmd without calling Quarto, so the step runs anywhere. It returns the path of the file it wrote.

report_path <- tf_render_report(theory, tempfile(fileext = ".qmd"))
basename(report_path)
[1] "file1faf104f4873.qmd"

tf_osf_push() deposits the audit dossier on an Open Science Framework node, as <id>.dossier.md. It defaults to dry_run = TRUE, which assembles the request and returns it without sending anything, so the planned deposit can be inspected offline and without a token. What goes up is the dossier, since that is the machine-checkable bundle a reviewer needs. The report written just above stays on your own machine.

str(tf_osf_push(theory, node = "ab12c"))
List of 3
 $ dry_run: logi TRUE
 $ request:List of 4
  ..$ method       : chr "PUT"
  ..$ url          : chr "https://files.osf.io/v1/resources/ab12c/providers/osfstorage/?kind=file&name=panic-network-2026.dossier.md"
  ..$ filename     : chr "panic-network-2026.dossier.md"
  ..$ content_bytes: int 1669
 $ note   : chr "set dry_run=FALSE with a valid token and node to perform the upload"

The returned request records the method, the destination URL, the filename the deposit would create and the size of the dossier in bytes, so the step can be checked before anything leaves the machine. A live deposit needs dry_run = FALSE together with a token and the node id, and that is the only path that touches the network.

tf_osf_push(
  theory,
  node = "ab12c",
  token = Sys.getenv("OSF_PAT"),
  dry_run = FALSE
)

Visualising the theory

tf_diagram() exports several views of the same object. The workflow view traces the lifecycle from constructs, through propositions and predictions, to the recorded test outcomes, so the path from a claim to its planned test is visible in one figure.

cat(tf_diagram(theory, "workflow"))
digraph workflow {
  graph [rankdir=LR, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  subgraph cluster_build {
    label="building";
    style="rounded";
    color="#C4D1D9";
    fontcolor="#5B7285";
    "c_arousal" [label="Physiological\narousal", fillcolor="#E4F1F1", color="#1E7B7B"];
    "c_perceived_threat" [label="Perceived threat", fillcolor="#E4F1F1", color="#1E7B7B"];
    "c_avoidance" [label="Avoidance\nbehaviour", fillcolor="#E4F1F1", color="#1E7B7B"];
  }
  subgraph cluster_relate {
    label="propositions";
    style="rounded";
    color="#C4D1D9";
    fontcolor="#5B7285";
    "prop_p1" [label="p1\nincreases", fillcolor="#FBF1DC", color="#9C6B14"];
    "prop_p2" [label="p2\nincreases", fillcolor="#FBF1DC", color="#9C6B14"];
    "prop_p3" [label="p3\ncauses", fillcolor="#FBF1DC", color="#9C6B14"];
  }
  subgraph cluster_predict {
    label="predictions";
    style="rounded";
    color="#C4D1D9";
    fontcolor="#5B7285";
    "pred_pred1" [label="pred1\npoint", fillcolor="#E7EDF5", color="#33567A"];
    "pred_pred2" [label="pred2\ninterval", fillcolor="#E7EDF5", color="#33567A"];
    "pred_pred3" [label="pred3\ndirectional", fillcolor="#E7EDF5", color="#33567A"];
  }
  subgraph cluster_test {
    label="testing";
    style="rounded";
    color="#C4D1D9";
    fontcolor="#5B7285";
    "outcome_pred1" [label="pred1\npassed", fillcolor="#E5F2E7", color="#3E7A46"];
  }
  "c_arousal" -> "prop_p1";
  "c_perceived_threat" -> "prop_p2";
  "c_perceived_threat" -> "prop_p3";
  "prop_p1" -> "pred_pred1";
  "prop_p3" -> "pred_pred1";
  "prop_p2" -> "pred_pred2";
  "prop_p2" -> "pred_pred3";
  "pred_pred1" -> "outcome_pred1";
}

Rendered with tf_render_diagram(), which passes the DOT to the DiagrammeR engine, the same view reads as a figure.

cat(
  '<div class="tf-figure tf-diagram">',
  tf_render_diagram(theory, "workflow", as = "svg"),
  '</div>',
  sep = ""
)
workflow cluster_build building cluster_relate propositions cluster_predict predictions cluster_test testing c_arousal Physiologicalarousal prop_p1 p1increases c_arousal->prop_p1 c_perceived_threat Perceived threat prop_p2 p2increases c_perceived_threat->prop_p2 prop_p3 p3causes c_perceived_threat->prop_p3 c_avoidance Avoidancebehaviour pred_pred1 pred1point prop_p1->pred_pred1 pred_pred2 pred2interval prop_p2->pred_pred2 pred_pred3 pred3directional prop_p2->pred_pred3 prop_p3->pred_pred1 outcome_pred1 pred1passed pred_pred1->outcome_pred1

The context view places the theory among the scope conditions under which it is claimed to hold and the registered rivals it is meant to outpredict.

cat(tf_diagram(theory, "context"))
digraph context {
  graph [rankdir=LR, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  "theory" [shape=ellipse, label="Network theory of\npanic disorder", fillcolor="#12283A", color="#12283A", fontcolor="#FFFFFF"];
  "c_arousal" [label="Physiological\narousal", fillcolor="#E4F1F1", color="#1E7B7B"];
  "theory" -> "c_arousal";
  "c_perceived_threat" [label="Perceived threat", fillcolor="#E4F1F1", color="#1E7B7B"];
  "theory" -> "c_perceived_threat";
  "c_avoidance" [label="Avoidance\nbehaviour", fillcolor="#E4F1F1", color="#1E7B7B"];
  "theory" -> "c_avoidance";
  "scope1" [shape=note, style="filled", label="adults", fillcolor="#FBF7EA", color="#B49B55"];
  "scope1" -> "theory" [style=dotted, label="holds within"];
  "scope2" [shape=note, style="filled", label="non-clinical\nbaseline", fillcolor="#FBF7EA", color="#B49B55"];
  "scope2" -> "theory" [style=dotted, label="holds within"];
  "scope3" [shape=note, style="filled", label="no beta-blocker\nmedication", fillcolor="#FBF7EA", color="#B49B55"];
  "scope3" -> "theory" [style=dotted, label="holds within"];
  "scope4" [shape=note, style="filled", label="waking hours", fillcolor="#FBF7EA", color="#B49B55"];
  "scope4" -> "theory" [style=dotted, label="holds within"];
  "scope5" [shape=note, style="filled", label="community sample", fillcolor="#FBF7EA", color="#B49B55"];
  "scope5" -> "theory" [style=dotted, label="holds within"];
  "alt_cognitive" [style="rounded,filled,dashed", label="Cognitive model of\npanic", fillcolor="#F1F1F1", color="#8A8A8A"];
  "theory" -> "alt_cognitive" [style=dashed, label="contrasts with"];
  "alt_biological" [style="rounded,filled,dashed", label="Biological model\nof panic", fillcolor="#F1F1F1", color="#8A8A8A"];
  "theory" -> "alt_biological" [style=dashed, label="contrasts with"];
}
cat(
  '<div class="tf-figure tf-diagram">',
  tf_render_diagram(theory, "context", as = "svg"),
  '</div>',
  sep = ""
)
context theory Network theory ofpanic disorder c_arousal Physiologicalarousal theory->c_arousal c_perceived_threat Perceived threat theory->c_perceived_threat c_avoidance Avoidancebehaviour theory->c_avoidance alt_cognitive Cognitive model ofpanic theory->alt_cognitive contrasts with alt_biological Biological modelof panic theory->alt_biological contrasts with scope1 adults scope1->theory holds within scope2 non-clinicalbaseline scope2->theory holds within scope3 no beta-blockermedication scope3->theory holds within scope4 waking hours scope4->theory holds within scope5 community sample scope5->theory holds within

The pipeline view links each prediction to its recorded test outcome, so predictions still awaiting a test are visible as loose ends.

cat(tf_diagram(theory, "pipeline"))
digraph pipeline {
  graph [rankdir=LR, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  "pred1" [label="pred1\npoint", fillcolor="#E7EDF5", color="#33567A"];
  "pred2" [label="pred2\ninterval", fillcolor="#E7EDF5", color="#33567A"];
  "pred3" [label="pred3\ndirectional", fillcolor="#E7EDF5", color="#33567A"];
  "result_pred1" [label="passed", fillcolor="#E5F2E7", color="#3E7A46"];
  "pred1" -> "result_pred1";
}
cat(
  '<div class="tf-figure tf-diagram">',
  tf_render_diagram(theory, "pipeline", as = "svg"),
  '</div>',
  sep = ""
)
pipeline pred1 pred1point result_pred1 passed pred1->result_pred1 pred2 pred2interval pred3 pred3directional

The provenance view draws the build log as a digraph, so the record of how the theory reached its current state travels with the object itself.

cat(tf_diagram(theory, "provenance"))
digraph provenance {
  graph [rankdir=TB, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  "n1" [label="tf_construct\nRegistered three\nconstructs."];
  "n2" [label="tf_proposition\nLinked constructs into a\nfeedback network."];
  "n3" [label="tf_predict\nDerived three predictions\nfrom the propositions."];
  "n1" -> "n2";
  "n2" -> "n3";
}
cat(
  '<div class="tf-figure tf-diagram">',
  tf_render_diagram(theory, "provenance", as = "svg"),
  '</div>',
  sep = ""
)
provenance n1 tf_constructRegistered threeconstructs. n2 tf_propositionLinked constructs into afeedback network. n1->n2 n3 tf_predictDerived three predictionsfrom the propositions. n2->n3

The venn view takes each construct’s boundary conditions as a set and returns an SVG showing where those scopes coincide and where they part company, with each region labelled by the number of conditions in it. It renders inline because the output is a self-contained image.

cat('<div class="tf-figure">', tf_diagram(theory, "venn"), '</div>', sep = "")
Construct scope overlapPhysiological arousalPerceived threatAvoidance behaviour1011011

Two further views summarise the scoring. The rigour view draws the checklist as a status grid, colouring each item by its result and reporting the aggregate score and the gate.

cat('<div class="tf-figure">', tf_diagram(theory, "rigour"), '</div>', sep = "")
Rigour checklistaggregate score 84.8, gate passfalsifiabilitypassprecisionpassrisk_severitypassparsimonypassnon_redundancypassconstruct_claritypassscopepasslogical_whypasscausal_testabilitypassdiagnosticitypassformalisationpassderivation_chainpass

The severity view draws one bar per prediction, scaled by its computed severity, so the riskier tests stand out.

cat(
  '<div class="tf-figure">',
  tf_diagram(theory, "severity"),
  '</div>',
  sep = ""
)
Prediction severitypred11.000pred20.700pred30.300

The development_roadmap view turns the same checklist into a worklist by keeping only the items that still fail or warn, and it orders that worklist the way the work should be done. Whatever gates the theory comes first, heaviest check first, and the advisory items follow. Each step carries its number, the criterion it is measured against and whether missing it blocks the gate or is merely advisory, so the figure amounts to a plan of work. The hub names the theory and its current standing. The panic network passes every check, so its roadmap reduces to that hub and an all checks pass node, and the deliberately weak theory shipped alongside it shows the worklist in full.

weak <- tf_read(
  system.file("fixtures/weak-theory.theory.yaml", package = "theoryforge")
)
cat(tf_diagram(theory, "development_roadmap"))
digraph development_roadmap {
  graph [rankdir=TB, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  "roadmap" [shape=ellipse, label="Network theory of\npanic disorder\nscore 84.8, gate pass", fillcolor="#12283A", color="#12283A", fontcolor="#FFFFFF"];
  "all_checks_pass" [label="all checks pass", fillcolor="#E5F2E7", color="#3E7A46"];
  "roadmap" -> "all_checks_pass";
}
cat(tf_diagram(weak, "development_roadmap"))
digraph development_roadmap {
  graph [rankdir=TB, bgcolor="transparent", fontname="Helvetica", fontsize=11, pad="0.2", nodesep="0.3", ranksep="0.45"];
  node [fontname="Helvetica", fontsize=11, shape=box, style="rounded,filled", color="#33567A", fillcolor="#F2F6F9", fontcolor="#12283A", penwidth=1.1, margin="0.16,0.1"];
  edge [fontname="Helvetica", fontsize=10, color="#7B909F", fontcolor="#0F6E6E", arrowsize=0.7];
  "roadmap" [shape=ellipse, label="An underspecified\nmotivation theory\nscore 12.0, gate blocked", fillcolor="#12283A", color="#12283A", fontcolor="#FFFFFF"];
  "falsifiability" [label="1. falsifiability\nAt least one\nprediction forbids an\nobservation\nblocks the gate", fillcolor="#F9E5E4", color="#B2453C"];
  "derivation_chain" [label="2. derivation_chain\nEach prediction is\ngraph-reachable from\npropositions\n(reachability only)\nblocks the gate", fillcolor="#F9E5E4", color="#B2453C"];
  "precision" [label="3. precision\nPredictions are\npoint/interval, not\nmerely directional\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "risk_severity" [label="4. risk_severity\nMean prediction\nseverity above\nthreshold\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "construct_clarity" [label="5. construct_clarity\nEvery construct has\ndefinition +\nmeasurement + boundary\nconditions\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "logical_why" [label="6. logical_why\nEach proposition\nstates a mechanism,\nnot just a correlation\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "scope" [label="7. scope\nBoundary conditions\nexplicitly stated\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "causal_testability" [label="8. causal_testability\nAt least one\nproposition states a\ncausal relation\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "diagnosticity" [label="9. diagnosticity\nAt least one\nprediction\ndiscriminates from a\nregistered alternative\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "formalisation" [label="10. formalisation\nA formal-model stub\nexists (warn-only at\nbuilding stage)\nadvisory", fillcolor="#FBF1DC", color="#9C6B14"];
  "roadmap" -> "falsifiability";
  "falsifiability" -> "derivation_chain";
  "derivation_chain" -> "precision";
  { rank=same; "precision" -> "risk_severity" -> "construct_clarity" [style=invis]; }
  "precision" -> "logical_why" [style=invis];
  { rank=same; "logical_why" -> "scope" -> "causal_testability" [style=invis]; }
  "logical_why" -> "diagnosticity" [style=invis];
  { rank=same; "diagnosticity" -> "formalisation" [style=invis]; }
}
cat(
  '<div class="tf-figure tf-diagram">',
  tf_render_diagram(weak, "development_roadmap", as = "svg"),
  '</div>',
  sep = ""
)
development_roadmap roadmap An underspecifiedmotivation theoryscore 12.0, gate blocked falsifiability 1. falsifiabilityAt least oneprediction forbids anobservationblocks the gate roadmap->falsifiability derivation_chain 2. derivation_chainEach prediction isgraph-reachable frompropositions(reachability only)blocks the gate falsifiability->derivation_chain precision 3. precisionPredictions arepoint/interval, notmerely directionaladvisory derivation_chain->precision risk_severity 4. risk_severityMean predictionseverity abovethresholdadvisory logical_why 6. logical_whyEach propositionstates a mechanism,not just a correlationadvisory construct_clarity 5. construct_clarityEvery construct hasdefinition +measurement + boundaryconditionsadvisory scope 7. scopeBoundary conditionsexplicitly statedadvisory diagnosticity 9. diagnosticityAt least onepredictiondiscriminates from aregistered alternativeadvisory causal_testability 8. causal_testabilityAt least oneproposition states acausal relationadvisory formalisation 10. formalisationA formal-model stubexists (warn-only atbuilding stage)advisory

All of these views are deterministic. The DOT strings render with any Graphviz engine, and the SVG views open in any browser. Together with the nomological_net and causal_dag views introduced in the Get started vignette, they complete the set of diagram types that tf_diagram() exports, each documented in ?tf_diagram.