Changelog

erplots 0.1.1

No user-facing changes. Fixes a documentation issue flagged by CRAN’s Debian pretest check: ?er_style used @param on a page with no attached function, producing an Rd file with \arguments but no \usage (NOTEd by a stricter R-devel Rd check). The argument descriptions now live in a plain @section Arguments: instead.

Initial CRAN release.

er_plot() builds a fluent, pipe-based specification for exposure-response plots, generalised across binary, continuous, and count responses (response_type = c("auto", "binary", "continuous", "count"), auto-detected when not supplied).
Six pipeline verbs attach layers to the specification – nothing is drawn until er_plot_build()/print()/plot() – and pipe order never affects the built plot:
- er_plot_add_model() – a model curve/ribbon.
- er_plot_add_summary() – a corner-placed text/label annotation (a model-derived statistic, or a plain observation count).
- er_plot_add_quantiles() – a quantile-binned response-rate/mean summary with confidence interval.
- er_plot_add_data() – a raw-data layer, either an overlay drawn on the main panel or one or more panels stacked below it.
- er_plot_add_groups() – stacked panels showing the exposure distribution per group variable (the one additive, non-singleton layer).
stratify_by splits colour/facet encoding across strata, following a documented colour/facet precedence rule (see vignettes/articles/design.Rmd).
er_plot_theme() styles labels, titles, axis limits, discrete/ continuous colour and fill palettes, formatters, the legend key glyph, and relative panel heights, without changing which variable drives which aesthetic.

Any model implementing er_predict() can be visualised; additionally implementing er_simulate() and/or er_summary() enables uncertainty spaghetti plots/VPCs and model-derived summary annotations. See ?er_model_interface.
erplots never fits a model itself – it is designed to work alongside companion packages that implement the interface, such as erglm (GLM-based exposure-response models) and emaxnls (Emax/sigmoidal dose-response models).

Every layer-adding function accepts a style argument (a er_style_*() builder function) that can be swapped for another built-in or a fully custom builder, with a documented interface (?er_style) and self-declared metadata via er_style_tag().
Built-in builders cover multiple visual idioms per layer, e.g. ribbon/line/spaghetti model curves, p-value/n/coefficients/ goodness-of-fit summaries, errorbar/pointrange quantile summaries (with boundary-labelled _vlines variants), overlay/hexbin/ boxjitter data layers, and boxplot/violin/histogram/boxjitter/ violinjitter group panels.

er_vpc() |> er_vpc_add_observed() |> er_vpc_add_simulated() mirrors er_plot()’s object/layer/builder architecture for building visual predictive checks, with an optional stratify_by for faceted panels.
Three visual idioms are available via style: an adaptive mean/ errorbar default, a continuous-x percentile-band idiom, and an adaptive quantile-errorbar idiom.
er_vpc_theme() styles labels, titles, axis limits, and formatters.

erplots_data – 4,000 simulated subjects spanning three exposure measures and five response columns (continuous, binary, and count), built to exercise every response type and modelling scenario used in the package’s documentation and vignettes.