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; 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() -- the shared tidy estimate table (methods for lm,
  glm, merMod and data frames; falls back 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 -- the brand and accent
  colours, qualitative palette, base font size and family, and the colour used
  for missing values -- which every plot and scale then honours.

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.
• A pass over every plot 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()/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) -- no more 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 -- conditionunrelated becomes condition, word_frequency becomes
  word frequency -- in coefficient_plot(), compare_models()
  and frequentist_bayesian_plot() (read from the model); 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(): the log-rank annotation now renders a proper chi-squared
  and an italic p; the median guide is labelled "median "; and the
  y-axis title margin is tighter.
• 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
  -- reclaiming 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;
  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, "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.