Package index
Launch the app
Run the bundled Shiny UI locally — same interface as the hosted version, no upload limits, recordings stay on your machine.
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run_app() - Launch the Shinytone app locally
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normalise_f0() - Normalise f0 by speaker
Outlier and artefact inspection
Token-level outlier detection by speaker z-score and by speaker x tone level, plus sample-level pitch-tracking artefacts (Sundberg 1973, Steffman & Cole 2022).
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inspect_f0() - Inspect f0 data for token-level outliers and sample-level jumps
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flag_outliers() - Flag per-token f0 outliers using by-speaker z-scores
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flag_level_outliers() - Flag tokens whose overall f0 level is unusual for their speaker and tone
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flag_pitch_jumps() - Flag sample-to-sample f0 jumps within tokens
Contour modelling
Token-level polynomial fits, mixed-effects growth-curve analysis, and GAMMs over tone contours.
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fit_polynomial() - Fit Legendre polynomials to f0 contours, token by token
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fit_gca() - Fit a Growth Curve Analysis (GCA) model to f0 contours
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predict_gca() - Predict population-level f0 contours from a GCA fit
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fit_gamm() - Fit a Generalised Additive Mixed Model (GAMM) to f0 contours
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predict_gamm() - Predict population-level f0 smooth curves from a GAMM fit
Chao tone numerals
Convert mean or predicted contours into Chao tone numerals (reference-line, interval-based, robust FOR methods).
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compute_mean_contour() - Compute the per-tone mean f0 contour from long-format data
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contour_to_chao() - Convert per-tone contours to Chao tone numerals
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classify_contour() - Classify a Chao tone numeral string as a shape
Bundled data
A small citation-tone corpus that ships with the package for examples, vignettes, and the Shiny app’s “Try with our sample data” button.
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sample_f0 - Sample f0 contour dataset
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shinytoneshinytone-package - shinytone: A Citation Tone Research Hub