erplots 0.1.2

Addresses CRAN reviewer feedback on the 0.1.1 submission. User-facing changes:

• er_plot_add_data()’s two jittered builders (er_style_data_overlay(), er_style_data_boxjitter()) no longer hard-code a specific RNG seed (previously a literal 1234L in R/er-plot-layer.R, used so that repeated plot() calls on the same object always showed identical jitter). Seeding is now opt-in only: pass seed = <value> through er_plot_add_data()’s own ... for reproducible jitter across rebuilds of the same object; with no seed (the default), jitter draws from the ambient RNG stream and differs from one build to the next, like any other jittered geom.
• er_plot_add_groups()’s jittered builders (er_style_group_boxjitter(), er_style_group_violinjitter()) gain the same opt-in seed support, for consistency – their jitter previously had no seed control at all. withr moves from Suggests to Imports to support this.

erplots 0.1.1

• No user-facing changes. Fixes a documentation issue flagged by CRAN’s Debian pretest check.

erplots 0.1.0

Initial CRAN submission.

er_plot(): the plotting mini-language

• 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.

The model interface

• 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).

The builder system

• 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(): the visual predictive check mini-grammar

• 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.

Bundled example dataset

• 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.