Appendix C — Standardised Option Names Reference

Use these standardised names whenever the concept applies. Do not invent synonyms — consistency across modules enables a coherent syntax mode and simplifies code review.

C.1 Naming Rules Recap

1. camelCase — no underscores, dots, or kebab-case.
2. Singular nouns — confidenceInterval, not confidenceIntervals.
3. Abbreviations are words — capitalise only the first letter: Ci, Bf, Mcmc, Se, Qq, Pp.
4. Nouns, not verbs — histogramPlot, not plotHistogram.
5. Reserved names — data and formula must not be used as option names.

C.2 General Options

Name	Purpose
ci	Confidence/credible interval checkbox
ciLevel	Confidence/credible interval level (e.g., 0.95)
se	Standard error
sd	Standard deviation
predictionInterval	Prediction interval checkbox
predictionIntervalLevel	Prediction interval level
dependent	Dependent variable
fixedFactors	Fixed factors variable list
randomFactors	Random factors variable list
covariates	Covariates variable list
weights	Weights variable
terms	Terms (interactions); combine as needed: modelTerms, marginalMeanTerms, plotTerms
naAction	Missing values handling. Values: pairwise, listwise, perAnalysis, perDependent
alternative	Alternative hypothesis. Values: twoSided, greater, less
testValue	Test value for one-sample tests
vovkSellke	Vovk-Sellke maximum p-ratio checkbox
bootstrap	Bootstrapping checkbox
bootstrapSamples	Number of bootstrap samples
bootstrapType	Type of bootstrap
setSeed	Set seed checkbox
seed	Seed value
xAxis / yAxis	Variable on x/y axis
xAxisLabel / yAxisLabel	Custom axis label text
rSquared	R² checkbox
effectSize	Effect size checkbox
pValue	p-value
max / min	Maximum / minimum
colorPalette	Colour palette dropdown
*SavedToData	Suffix for options that save columns to the dataset (e.g., predictionSavedToData)

C.3 Bayesian Options

Name	Purpose
samplingMethod	MCMC sampling type. Values: auto, manual, bas, mcmc
samples	Number of MCMC iterations
burnin	Burn-in samples
thinning	Thinning factor
chains	Number of chains
bf	Bayes factor
bfType	Bayes factor type (use via BayesFactorType{} component)
priorPosteriorPlot	Prior/posterior plot checkbox
priorPosteriorPlotAdditionalInfo	Additional info on prior/posterior plot
bfRobustnessPlot	Robustness plot checkbox
bfRobustnessPlotAdditionalInfo	Additional info on robustness plot
bfSequentialPlot	Sequential analysis plot checkbox
bfSequentialPlotRobustness	Robustness check in sequential plot

C.4 Bayesian Prior Options

Name	Purpose
cauchyPrior	Cauchy prior checkbox/radio
cauchyPriorLocation	Cauchy prior location
cauchyPriorScale	Cauchy prior scale
normalPrior	Normal prior checkbox/radio
normalPriorMean	Normal prior mean
normalPriorSd	Normal prior SD
tPrior	t-distribution prior checkbox/radio
tPriorLocation	t prior location
tPriorScale	t prior scale
tPriorDf	t prior degrees of freedom

C.5 Nesting Conventions

C.5.1 Parent-child Prefixing

When a child option belongs to a parent, prefix with the parent name:

analysis(
  errorBar        = TRUE,
  errorBarType    = "ci",
  errorBarCiLevel = 0.95
)

C.5.2 Structurally Nested Options

Options nested inside a TabView or similar structure that are already grouped in a list do not need a prefix:

analysis(
  models = list(
    list(name = "Model 1", syntax = "y ~ x1"),
    list(name = "Model 2", syntax = "y ~ x1 + x2")
  )
)

C.5.3 RadioButton Values

Radio button values are character strings — they must follow the same camelCase rules:

RadioButtonGroup
{
    name: "alternative"
    RadioButton { name: "twoSided"; label: qsTr("Group 1 ≠ Group 2"); checked: true }
    RadioButton { name: "greater";  label: qsTr("Group 1 > Group 2")                }
    RadioButton { name: "less";     label: qsTr("Group 1 < Group 2")                }
}