Package: janusplot 0.2.0

Max Moldovan

janusplot: Asymmetric Smoothed-Association Matrices via GAM Fits

Render a pairwise, asymmetric smoothed-association matrix of continuous variables. Each cell shows the fitted spline from an 'mgcv' generalised additive model, with the upper triangle displaying 'gam(x_j ~ s(x_i))' and the lower triangle 'gam(x_i ~ s(x_j))'. Unlike Pearson's correlation matrix, the visualisation is intentionally asymmetric, revealing heteroscedasticity, leverage, and directional non-linearity that a single scalar correlation hides. An asymmetry index and a 24-category shape taxonomy quantify the directional difference and qualitative form of each fitted smooth.

Authors:Max Moldovan [aut, cre, cph]

janusplot_0.2.0.tar.gz
janusplot_0.2.0.zip(r-4.7-any)janusplot_0.2.0.zip(r-4.6-any)janusplot_0.2.0.zip(r-4.5-any)
janusplot_0.2.0.tgz(r-4.6-any)janusplot_0.2.0.tgz(r-4.5-any)
janusplot_0.2.0.tar.gz(r-4.7-any)janusplot_0.2.0.tar.gz(r-4.6-any)
janusplot_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
janusplot/json (API)

# Install 'janusplot' in R:
install.packages('janusplot', repos = c('https://max578.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/max578/janusplot/issues

Pkgdown/docs site:https://max578.github.io

Datasets:

On CRAN:

Conda:

5.04 score 385 downloads 10 exports 22 dependencies

Last updated from:b33d3da192 (on main). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK164
source / vignettesOK514
linux-releaseOK202
macos-releaseOK136
macos-oldrelOK128
windows-develOK126
windows-releaseOK140
windows-oldrelOK121
wasm-releaseOK157

Exports:janusplotjanusplot_datajanusplot_direction_testjanusplot_shape_cutoffsjanusplot_shape_hierarchyjanusplot_shape_metricsjanusplot_shape_sensitivityjanusplot_shape_sensitivity_plotjanusplot_shape_sensitivity_shapesjanusplot_shape_sensitivity_summary

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecycleMatrixmgcvnlmepatchworkR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Asymmetric Smoothed-Association Matrices
Why Pearson is not enough | Quick start | Non-linear detection | Asymmetry -- a heteroscedastic example | Partial smooths (controlling for covariates) | Changing the palette | Handling missing data | Scaling up -- order = "hclust" | Programmatic access -- janusplot_data() | Shape metrics explained | Derivative views: theoretical justification and applied use | What derivatives reveal that the fit hides | Estimation -- the LP matrix | Noise amplification and why we cap at $k = 2$ | Applied use: gain estimation and dose--response | References cited in this section | Limitations | Citation
Rendered fromjanusplot.Rmdusingknitr::rmarkdown

Last update: 2026-08-25
Started: 2026-04-21

Shape-recognition sensitivity study
Design | Pre-registered hypotheses | Precomputed demo | Recovery curves (headline figure) | Archetype confusion | Archetype-level accuracy grid | Numerical summary | Running your own sweep | Custom shape subsets + cutoffs | References
Rendered fromshape-recognition-sensitivity.Rmdusingknitr::rmarkdown

Last update: 2026-08-25
Started: 2026-04-21