depictr 0.3.0

Auditing a finished figure

• New check_figure(), an accessibility and honesty audit of the figure you are about to submit. Until now the package could vouch for its palette and say nothing about a finished plot, which is the thing a reader sees. Give it anything a depictr function returns, including a plot extended afterwards with +, and it introspects the build and returns a tidy table. The rows cover the separability of the encoding colours under each dichromacy and in greyscale, the smallest text size against a stated physical output width, the WCAG contrast of the text and of the geometry against their backgrounds, and whether any distinction is carried by colour alone. Every row carries the value it measured beside the threshold it was measured against, so a verdict can be argued with.
• The colour-vision helpers are exported. simulate_cvd() and palette_safety() were internal here while the Python twin exported both, so the advertised parity did not hold. They now match in name, arguments, return shape and refusal wording. simulate_cvd() gains a severity argument and returns lower-case hex, as the Python twin does. palette_safety() returns the full report, naming the worst condition, the closest pair and the verdict. The old return was a bare vector of distances.
• The accessibility claim has been narrowed to what is true. The default eight-colour palette clears every colour-vision check and fails the new greyscale check: its orange (#e69f00) and sky blue (#56b4e9) differ by 0.79 in CIE lightness, so a black-and-white printer renders them as the same grey. The Okabe-Ito guarantee is about hue confusion and was never a claim about greyscale. The threshold stays where it is, and vignette("depictr") now states the limitation where the claim is made, so the package’s own defaults are held to the same standard as anybody else’s.

Figures that misreported the data

• quantile_residuals() produced nonsense for a cbind(successes, failures) binomial model. The two-column response matrix was flattened to a vector and the raw success counts were then multiplied by the trial totals a second time, so the residuals of a well-specified model centred far from zero and the Q-Q diagnostic looked catastrophically misspecified. The matrix response now supplies its counts and trial totals directly, and the residuals are standard normal again where they should be.
• ridgeline_plot() stacked its overlaps upside down. The row sort meant to draw the top ridge first is a no-op, because ggplot2 draws ribbon groups in factor-level order, so each upper ridge painted over the one below it, the opposite of the conventional ridgeline overlap. The draw order is now carried by the group aesthetic, with the colour assignment unchanged.
• random_effects_plot(sort = TRUE) froze every facet in the first facet’s order. With more than one term the level factor was shared across panels, so only the first panel came out sorted and the rest zig-zagged. Each facet now orders its own levels, with the plain level names kept on the axis.
• A user-supplied title in power_curve_plot() is no longer run through format_terms(), which turned a colon into a multiplication sign and blanked underscores. Only a title recovered from the power-curve object, which is a raw term name, is tidied.
• survival_plot() drew a phantom arm for a group that does not exist. An NA in group became a level of its own, matching no observation, so the plot gained an all-censored curve for a group nobody was in, and under logrank = TRUE it failed outright. Missing groups are now dropped with a message saying how many.
• survival_plot() silently discarded non-finite follow-up times. Dropping a case from a Kaplan-Meier fit changes the denominator, and so every step of the curve and every cell of the number-at-risk table, while the figure carries no trace of it. It now refuses them, with the same message as the Python twin, since whether to drop or impute is the analyst’s decision to make. This replaces a silent drop, so it is a behaviour change for anyone who relied on the old handling.
• depictr_palette() interpolated past its accessibility guarantee without saying so. Beyond the eight Okabe-Ito base colours the palette is a ramp, and the colour-vision-deficiency guarantee that is this package’s reason for existing stops holding, so the interpolated palette fails the package’s own safety check. It now warns at the point of interpolation, and only for the built-in palette, since a user-supplied one carries no such claim. The documentation is qualified to match.
• summary_table() counted missing-group records in Overall and in no group column, so the per-group sizes silently fell short of the headline N. They now get a Missing column of their own.
• A seeded plot leaked its seed into the caller’s random stream in a session that had not yet drawn a random number, so the documented reproducibility guarantee quietly failed in exactly the fresh session that would rely on it.
• compare_models(facet = TRUE) now honours depictr_options(reference = ) for its per-panel reference line. seasonal_plot() no longer labels a frequency-7 series Mon..Sun, an alignment a plain ts cannot know. And a mistyped labels key now warns, so the raw parameter name no longer sits on the plot.

Degenerate input

• survival_plot() now says when it drops observations with a missing status, in the wording of the missing-group message. The drop was previously silent. A status that is missing for every observation is an error. This brings the third kind of incomplete survival input into line with the other two, a missing group being announced and a non-finite time refused.
• silhouette_plot() checks that clusters has one entry per row of data before dropping incomplete rows. A vector sized to the complete rows used to slip past the late check, because subsetting it with the logical index padded it with NA, and then died in the distance computation. It is now refused with the same message cluster_plot() uses.
• tidy_estimates() no longer fails on a rank-deficient lm with a raw “differing number of rows” error. confint() keeps aliased terms as NA rows while coef(summary()) drops them, so the intervals are now cut to the estimated terms, with a message naming the aliased terms that were left out.
• cluster_plot() and k_diagnostic() drop zero-variance columns with a message when scale = TRUE, as correlation_heatmap() already did, so a raw k-means error no longer escapes. k_diagnostic() now names the values of k_range it cannot evaluate instead of dropping them from the search without a word, and explore_pairs() labels an undefined correlation n/a where it once printed r = NA beside a raw stats::cor() warning.

Metadata and documentation

• The declared minimum dependency versions are now installed and tested by a CI job. The patchwork floor is raised from 1.2.0 to 1.3.0. That was verified by running it: 1.2.0 cannot run model_report() at all, because patchwork::free(type =, side =) arrived in 1.3.0.
• gain_plot() documents that the perfect-model reference line is drawn for a single model only, which is what the code has always done, and a test now pins it. The line bends at the prevalence of the outcome, and overlaid models need not share a prevalence.
• model_fit_table() documents that a single model is enough, which is what the code always accepted, and raincloud_plot() no longer claims to be built from base graphics primitives: like ridgeline_plot(), it is base R and ggplot2 alone.
• ?depictr again lists every exported function (scale_fill_depictr() and the scale_color_depictr() alias were missing), the README no longer describes the Python package as a feature-parity twin (its own README says otherwise), CONTRIBUTING no longer claims the maintenance workflows close their own issues, and the standardise = TRUE axis label now names the x-only convention the figure uses.
• The README no longer opens with a link to the documentation site, which on the site’s own home page pointed the reader at the page in front of them.
• Every vignette now turns console colour off and fixes the console width while it renders. pkgdown passes the calling terminal’s colour support into its build subprocess, so a coloured message or error would otherwise reach the reader as escape sequences in the middle of the text.

depictr 0.2.2

Examples that match the chart

• The precision-recall, gain and lift examples move to an outcome with a scarce positive class, which is the case those charts are documented for. They previously ran on an outcome that was 94 per cent positive, so the gain curve sat on the diagonal and lift hovered at one.
• The calibration example fits a model and plots its predicted probabilities, since a reliability curve is a check on a fitted model.
• The power-curve article names the effect the shipped simulation actually covers, and its no-simr branch reads a summary derived from that simulation, so the two branches agree by construction.

Fixes

• vif_plot() restricts its scale to the severity levels present, which removes an empty key entry from the rendered figure.

depictr 0.2.1

Documentation

• The introductory vignette shows depictr_palette() returning the palette’s hex colours directly, ready for scale_fill_manual() or a base-graphics col = argument, and shows depictr_options() setting defaults for every later plot and returning the previous values, so the earlier look can be put back afterwards.
• vignette("diagnostics-and-uncertainty") saves an arranged panel with save_plot(), which writes at a print-ready 300 dpi by default and creates any missing directories.
• vignette("multivariate-and-survival") adds k_diagnostic(method = "gap"), which compares within-cluster dispersion against a null reference and so, unlike the other two criteria, can support k = 1.
• vignette("time-series") adds classical decomposition, which holds the seasonal component fixed across the series where STL lets it evolve from year to year.
• The optimizer_fixef_plot() and power_curve_plot() reference pages describe what each plot shows. Both pages previously described the prototype gists the plots grew from.
• On the documentation site, source chunks are set a little smaller than the output and the prose, so a typical line fits the narrower home and article columns without horizontal scrolling, and the copy button stays attached to its code block.

depictr 0.2.0

Fixes

• roc_curve_plot() rejects a ci that resolves to fewer than one bootstrap resample, with a clear error. Until then it drew an all-NA band and an [NA, NA] AUC annotation, saying nothing.

Data

• In wellbeing_survey, region now shifts stress and income, which flow through to life satisfaction. The region-grouped plots (faceted densities, ridgelines, the region dendrogram) therefore compare four distinct distributions where earlier they compared four samples of one. The bundled datasets are regenerated by data-raw/generate_datasets.R as before.

Citation

• The package citation (inst/CITATION and CITATION.cff) carries the Zenodo concept DOI, and the citation title uses sentence case.

Documentation

• The package overview (?depictr) lists every exported function and all five bundled datasets. Previously several functions and two datasets were missing.
• The vif_plot() example fits deliberately collinear predictors, so the plot shows inflated VIFs sitting above the threshold line. The earlier example produced near-identical bars around 1, with the line off the axis.
• depictr_options() describes what brand and accent actually drive, and depictr_palette() notes that qualitative colours interpolated beyond the base set are not guaranteed to stay distinguishable under colour-vision deficiency.
• lift_plot() documents its own top-right inside-legend corner rather than inheriting gain_plot()‘s bottom-right wording, and DESCRIPTION notes that composite panels return ’patchwork’ objects.
• References throughout the documentation follow APA 7 and carry DOIs, and spelling is consistently British (en-GB).
• The documentation site’s home page is restructured around a pitch, gallery and signposts. The time-series decomposition example draws its trend confidence band. LICENSE.md carries the full MIT text so the site’s licence page renders in full, as in the sibling packages.

Packaging and checks

• Example variants that render several multi-panel figures (posterior_plot(), residual_diagnostics_plot(), decompose_plot()) are wrapped in \donttest{} so each example file stays within CRAN’s five-second budget. The first call of every example still runs.
• CITATION.cff and the test artefact Rplots.pdf are excluded from the build tarball, and the test that produced Rplots.pdf draws to a null device instead.
• New tests pin the legend_inside gates. The legend moves inside the panel when a plot’s gate is satisfied, and the theme is left alone when it is not.

depictr 0.1.1

• Documentation and packaging polish, with no change to the plotting API.
• The documentation site adopts the shared house style used across the package family, with a citation page carrying a copyable and downloadable BibTeX entry.
• Consolidated to a single LICENSE file, and added community and contribution files.

depictr 0.1.0

First release. depictr is a unified, consistent toolkit of publication-ready plots spanning the whole analysis workflow. It grew out of, and generalises, three earlier plotting functions (frequentist_bayesian_plot, plot.fixef.allFit and powercurvePlot).

Exploring data

• explore_distribution(), explore_categorical(), explore_bivariate(), explore_pairs(), correlation_heatmap(), missingness_map(), outlier_plot(), raincloud_plot(), group_comparison_plot(), scatter_trend() and summary_table().
• estimation_plot() for estimation statistics: group effect sizes (mean differences, Cohen’s d / Hedges’ g) with bootstrap confidence intervals, in the spirit of the ‘new statistics’.
• ecdf_plot() (empirical cumulative distribution, optionally by group), ridgeline_plot() (overlapping per-group densities) and dumbbell_plot() (a connected two-group comparison across categories).
• explore_distribution() gains facet to draw one panel per group instead of overlaying them (much clearer beyond a few groups), and correlation_heatmap() gains reorder to cluster correlated variables together.

Multivariate, clustering and survival

• pca_plot() and scree_plot() (principal component analysis), cluster_plot() (k-means on principal-component axes) and dendrogram_plot() (hierarchical clustering), and survival_plot() (Kaplan-Meier curves with a number-at-risk table, median survival and an optional log-rank test, all computed in base R).
• silhouette_plot() and k_diagnostic() help choose and validate the number of clusters (silhouette widths, plus elbow and average-silhouette diagnostics).

Time series

• timeseries_plot() (one or more series with an optional moving average), acf_plot() (autocorrelation / partial autocorrelation) and decompose_plot() (trend / seasonal / remainder decomposition).
• seasonal_plot() (seasonal subseries) and ts_forecast() (a simple, dependency-free forecast with prediction intervals).

Model estimates and inference

• tidy_estimates() provides the shared tidy estimate table, with methods for lm, glm, merMod and data frames and a fallback to broom::tidy().
• coefficient_plot(), compare_models(), frequentist_bayesian_plot(), effects_plot(), interaction_plot(), random_effects_plot(), optimizer_fixef_plot() and model_fit_table().
• frequentist_bayesian_plot() now draws the full Bayesian posterior for each term as a half-eye density and overlays the matching frequentist point estimate and confidence interval, so the two inferential frameworks can be compared directly. It reads posterior draws from brmsfit, stanreg, draws/matrix objects or a data frame.

Diagnostics and classification

• residual_diagnostics_plot(), influence_plot(), qq_plot(), vif_plot(), roc_curve_plot(), pr_curve_plot(), gain_plot(), lift_plot(), calibration_plot() and confusion_matrix_plot().
• binned_residual_plot() (binned residuals for logistic and other GLMs, with approximate error bounds) and threshold_plot() (classification metrics across decision thresholds, highlighting Youden’s J and the maximum-F1 cut-off).

Uncertainty and power

• posterior_plot() summarises posterior draws with a choice of styles ("halfeye", "interval", "gradient" or "dots") and can annotate a region of practical equivalence (ROPE) and the probability of direction.
• power_curve_plot() for power-analysis curves (e.g. from simr).

Theming and reporting

• theme_depictr(), depictr_palette(), scale_colour_depictr() (and scale_color_depictr(), scale_fill_depictr()), palette_preview(), format_terms(), model_report() (a one-figure model overview), arrange_plots() and save_plot().
• depictr_options() sets package-wide defaults once, covering the brand and accent colours, qualitative palette, base font size and family, and the colour used for missing values. Every plot and scale then honours them.

Layout and legibility

• coefficient_plot(), compare_models(), posterior_plot() and frequentist_bayesian_plot() gain a facet/scales option that lays each term out in its own free-scaled panel, so terms on very different scales (a large intercept alongside small slopes) stay legible instead of being squished onto the zero line. frequentist_bayesian_plot() uses this layout by default.
• Every plot has had a pass for legibility. silhouette_plot() cluster labels no longer clip. raincloud_plot() uses one colour per group across all layers. dendrogram_plot() hides leaf labels for large trees. confusion_matrix_plot() picks each label’s colour from the tile luminance. gain_plot() and lift_plot() label their reference lines. timeseries_plot() shows a single legend, and k_diagnostic() now returns the diagnostic curve as a plot.
• coefficient_plot() gains standardise, scaling each coefficient by its predictor’s standard deviation so magnitudes are comparable. model_report() uses it by default, removing the empty band in its coefficient panel.
• vif_plot() shows the ordinary VIF (not its square root) for single-df terms, scales the axis to the data, and draws a single clearly-labelled threshold line (reported in the caption when it is off-axis), leaving no wide empty band or hard-to-read guides.
• seasonal_plot(style = "season") reverses its sequential legend so the darkest, most-recent cycle sits at the top, matching the plotted order.
• Factor coefficient names are prettified by default to the effect (variable) name in coefficient_plot(), compare_models() and frequentist_bayesian_plot(), where they are read from the model, so conditionunrelated becomes condition and word_frequency becomes word frequency. optimizer_fixef_plot() and posterior_plot() gain a labels argument for the same. Any user-supplied labels take precedence. pca_plot() likewise shows underscores in its loading-arrow labels as spaces (soil_ph -> soil ph).
• Redundant cluster legends are dropped: silhouette_plot() (the bands are labelled in place) and cluster_plot() when the centroids are labelled.
• survival_plot() has been tidied in several ways. The log-rank annotation renders a proper chi-squared and an italic p, formatted APA style (no leading zero, p < .001 below that threshold). The median guide is labelled median <value>. The y-axis title margin is tighter, and the colour legend and the number-at-risk table list the groups in the same order, following the group factor’s levels.
• A legend_inside argument (off by default) draws the legend inside the panel, over a semi-transparent background, in a corner the plot usually leaves empty, which reclaims the right-hand margin. It is offered by roc_curve_plot(), gain_plot(), lift_plot() (bottom-right / top-right of the curve), ecdf_plot(), survival_plot(), explore_distribution(), dumbbell_plot() and missingness_map(). For any other plot the same is one theme() call, and vignette("exploring-data") shows how, alongside tidying legend titles.
• theme_depictr() now centres legend titles over their keys, which reads more tidily than ggplot2’s default left alignment, especially for an inside or a top/bottom legend.
• estimation_plot() reserves more headroom above the lower panel so the effect-size annotation (Hedges’ g / Cohen’s d) is never clipped.
• scree_plot() colour-matches and names its dual axes, ‘Variance explained (bars)’ on the left and ‘Cumulative (line)’ on the right.
• Statistical letters are italic in annotations: the log-rank p, model_report()’s n and R, and estimation_plot()’s g / d.
• British (en-GB) spelling throughout: the crop_yield column is now fertiliser, coefficient_plot()/model_report() take standardise, and confusion_matrix_plot() takes normalise.

Data

• Five reproducibly simulated datasets: lexical_decision (counterbalanced priming experiment), wellbeing_survey (with realistic missingness), crop_yield (a fertiliser-by-treatment field trial), clinical_trial (right-censored survival with a rare adverse event) and monthly_sales (two seasonal retail series).

Accessibility

• The qualitative palette is based on the colourblind-safe Okabe-Ito set (led by the depictr brand blue), and depictr_palette() provides sequential and diverging variants. palette_preview() can show any one, or all three, and can simulate deuteranopia, protanopia or tritanopia so a palette’s legibility can be checked directly.

Notes

• Heavier modelling back-ends (lme4, broom, simr, survival, brms, posterior, ggdist, cluster, boot) are in Suggests and used only when available, so the package installs and checks without them. Vignettes draw on small precomputed model fits shipped in inst/extdata/, so they knit without a Bayesian or mixed-model toolchain.
• Functions with an optional seed (cluster_plot(), qq_plot() and residual_diagnostics_plot()) restore the caller’s random number generator state afterward, so passing one for reproducibility has no side effect on your own subsequent random draws.