Changelog

Source: NEWS.md

VizModules 0.4.0

New Modules

• Added a freqPlot module (dittoViz_freqPlotInputsUI(), dittoViz_freqPlotOutputUI(), dittoViz_freqPlotServer(), dittoViz_freqPlotApp()) wrapping [dittoViz::freqPlot()], for comparing the per-sample composition of a categorical variable across groups. Unlike the other modules it does not plot columns of the incoming data, it tabulates how often each level of the chosen variable occurs within each sample and plots those frequencies, one facet per level. The axis limits, statistics, point annotations and source download therefore all describe that summarised frequency table rather than the input rows.
  • Comes with a new example_composition demo dataset containing 1800 simulated single-cell records over twelve donors nested inside two conditions (and crossed with two batches).
• Added a ComplexHeatmap module (ComplexHeatmap_HeatmapInputsUI(), ComplexHeatmap_HeatmapOutputUI(), ComplexHeatmap_HeatmapServer(), ComplexHeatmap_HeatmapApp()) wrapping [ComplexHeatmap::Heatmap()]. Unlike the other plotly-based modules, its interactive output is delivered via the InteractiveComplexHeatmap package (sub-heatmap zoom, cell hover/click/select). The incoming data frame is converted to a numeric matrix (user-selected columns, with an optional row-name column), and a curated subset of Heatmap() parameters is exposed via UI inputs.
  • Row and column annotation tracks can be added on the “Annotations” tab dynamically. Row annotations come from extra columns in the input data frame; column annotations need a companion per-sample metadata table, supplied via data = list(matrix = <data.frame>, column_annotations = <data.frame>) instead of a plain data frame.
  • The interactive output can be split into independently-placed pieces — ComplexHeatmap_HeatmapMainOutputUI(), ComplexHeatmap_HeatmapSubOutputUI(), ComplexHeatmap_HeatmapInfoOutputUI() — for apps that want the main heatmap, sub-heatmap, and click/brush info panel in separate layout locations. ComplexHeatmap_HeatmapOutputUI() also has a ... passthrough for InteractiveComplexHeatmapOutput()’s layout, compact (a smaller-footprint mode that drops the sub-heatmap panel and floats the click/brush info near the cursor), and other arguments.
  • Comes with a new example_heatmap_matrix (30 genes x 12 samples, with row-annotation columns) and example_heatmap_column_data (companion per-sample metadata, for column annotations) demo datasets.

Improved/New Functionality

• The package now contains three agent skills under inst/skills/, installable into a project with the new exported use_vizmodules_skills(): vizmodules-app (wiring modules into an app), vizmodules-custom-module (building wrapper modules), and vizmodules-new-module (authoring a module in this package). They follow the Agent Skills SKILL.md convention, so GitHub Copilot, OpenAI Codex, Claude Code, and compatible tools can discover them. use_vizmodules_skills() gains a client argument ("agents" by default, or "github"/"claude") to install into .agents/skills/, .github/skills/, or .claude/skills/ as needed.
  • Benchmarked against the README’s LLM-instruction prompt over 18 paired runs (#341). Building an app the clear win - half the tokens (66k vs 123k) and 40% of the wall time, with identical correctness. Wrapping a module was inconclusive, and authoring a module was cost-neutral. Every run in both arms passed every assertion, so the skills’ measured value is efficiency on lookup-heavy work rather than improved output.
  • Each skill carries the traps that cost benchmark runs real time: useShinyjs() being required in a hand-built app for hide.inputs/hide.tabs to work, pandoc being required by create_source_download_handler(), stat.hide.ns defaulting to TRUE so an enabled Stats tab can draw nothing, and shiny::testServer() being unable to drive a plotly output.
• The dittoViz_yPlot module gained an “Annotations” tab that highlights and labels individual jitter points, matching the dittoViz_scatterPlot module (#340).
  • The annotation controls are now the exported helpers uniform_annotation_inputs_ui() and reset_annotation_inputs(), so other modules that draw individual points can pick them up, and dittoViz_scatterPlot now uses them rather than its own copy.
  • dittoViz_yPlot’s jitter positions are now drawn from a fixed seed, so they no longer reshuffle on every rebuild. Selections and annotations therefore stay attached to the points they were made on, and a plot redrawn with the same settings is reproducible.
  • Box/lasso selections are now matched to their points by index rather than by coordinates, so a label survives the rebuild that the selection itself triggers. Selections are cleared when the plot’s structure changes (Y data, grouping, color, shape, facet or plot types), since the indices only describe the layout they were captured on.
• Every module can now be initialized with an explicit group-to-color mapping via defaults (#334). Pass a named character vector under the module’s color input key, e.g. defaults = list(palette.colours = c(setosa = "red", virginica = "#0072B2")), and the color picker is seeded with it.
  • Precedence runs picker > defaults > the module’s stock palette, so a plot can open on a specific palette while every color stays editable, and groups the mapping does not name still get a sensible default. Reset restores the supplied mapping rather than the stock palette.
  • Keys are palette.colours for most modules, color.panel for dittoViz_scatterPlot, slice.colors for piePlot, and trace.colors for radarPlot; the ungrouped single-color controls (single.point.color, single.fill.color, single.color) and the continuous palette selectors (palette.name, gradient.palette) are now seeded from defaults too. Since individual defaults entries may be reactive, a parent app can also drive the palette from its own state. resolve_palette() gains a manual_colors argument implementing the layering.
• The figure builder now passes each panel’s defaults to the module server as well as to its inputs UI, so registry defaults can seed server-rendered controls such as the color picker.
• Source data downloads are now more robust and now limit to the data actually shown on the plot rather than the entire input dataframe. This makes download snappier and keeps plot source data contained, which is important for publication. The switch to viz_select_input (described below) also required some changes to handle empty vectors/NULL values appropriately.
• Individual defaults entries can now be a reactive() or reactiveVal(), letting a parent app drive a module parameter from its own state (#325). Previously the only route was update*Input() from the parent, which is an asynchronous client round-trip and so re-rendered the plot twice per change (a visible flicker). Reactive defaults are resolved server-side in the same reactive flush as the data, so the plot renders once, while the on-screen control stays populated and user-editable. An external change takes precedence over a value the user has typed, and Reset restores the reactive’s current value. Adds the exported helper setup_reactive_defaults(); setup_auto_update_logic() gains an optional params argument to consume its store, and get_default() now resolves reactive entries with isolate(). Modules with purely static defaults are unaffected. Not supported for the scatter module’s compound custom.models input.
• Wired up hover.data and hover.round.digits in the dittoViz_yPlot module (#317). When no columns are selected, the module reproduces dittoViz::yPlot()’s default hover content, so existing plots are unchanged.
• The dittoViz_yPlot module’s “Y Data” input can now take several columns at once (selecting more than one previously errored while computing the y-axis range).
  • New “Multivar Aesthetic” and “Multivar Split Dir” controls on the Facet tab expose dittoViz::yPlot()’s multivar.aes/multivar.split.dir, so the selected variables can each get their own facet (the default), sit side by side on the x-axis, or be mapped to the fill legend (in which case the colour picker keys off the variable names, since they are what is being coloured). The y-axis limits span every selected variable, the axis title drops the column name once it no longer describes the shared axis (keeping any adjustment, e.g. log2(z-score)), and the facet-specific handling (subplot spacing, boxplot dodging, shared axis titles) now also applies to variable facets.
  • Statistics are computed separately within each variable’s facet; the Stats tab is hidden for the “group” and “color” aesthetics, and when a split.by facet is combined with several variables, as significance brackets cannot be placed against those layouts without stuff getting hella complicated in ways the current stats implementation cannot yet handle. Ideally, this will be supported in the future but will take some thoughtful work to implement in a robust way.
• Every module select input is now a virtualised, searchable dropdown built on shinyWidgets::virtualSelectInput() (#330). Previously a select fed by a high-cardinality column (e.g. var in dittoViz_yPlot on a genome-wide table) rendered every option, producing a dropdown that was both slow and impossible to pick from even with max options set. Only the visible slice is rendered now, so tens of thousands of options stay usable, and long lists gain a search box automatically. Adds the exported helpers viz_select_input() and update_viz_select() for use in custom modules. Three widgets deliberately stay native because client-side JavaScript reads them directly: the figure builder’s “Panel labels” menu, multiColorPicker()’s palette picker, and multiDynamicInput()’s select rows.
• dataFilterServer() gains filter.max.options (default 50), capping how many options a factor column’s DataTables filter dropdown renders at once. Typing still searches the full set. Note that DT serialises every level of a factor column into the page regardless, so factor.char.cols = TRUE remains a poor fit for columns with very many distinct values.
• The dataFilter table’s controls now sit on a single row for better use of space. With col.visibility = TRUE the module used DataTables’ Blfrtip layout, which stacks the “Columns” button, the page-length select and the search box in three full-width blocks, wasting three rows of vertical space above the table. They now share one flex row with the search box aligned to the far end, styled by CSS the module ships itself.
• Tweaked multiColorPicker() layout slightly for easier tetrising into compact UIs. Elements should now reflow more appropriately to prevent label/control overlaps in narrow contexts.
• multiColorPicker() no longer reports a value for every step of a colour choice, so a dependent plot is rebuilt once per colour rather than dozens of times. A group’s swatch is a native <input type="color">, and the browser’s colour dialog previousl fired an event for each drag or click inside it (Chrome fires change just as often as input, rather than only on close). Now, the value is only reported when the input loses focus or the user moves the mouse outside it, preventing most unnecessary re-renders. Typing in a hex field is coalesced until the user pauses instead, while one-shot actions, i.e. palette swatches, “Apply”, “Reset”, selecting another group, and a hex code committed with Enter or by clicking away, still report immediately.
• Added ability to show/hide columns in the dataFilter module with DataTables’ built-in column visibility controls. This is useful for hiding columns that are not relevant to the user, or for hiding columns that are used for internal logic but not meant to be displayed. The hide.columns argument can be used to specify which columns to hide by default (by name or position), which also removes their filter boxes for a simpler interface, and col.visibility = TRUE adds a “Columns” button so users can toggle visibility via the DataTables UI. Hiding is display-only: hidden columns are still present in the returned filtered data, so downstream plotting modules can use them. The name/position lookup behind hide.columns is exposed as the new exported helper resolve_column_targets(), which turns column names into the zero-based targets indices any hand-rolled [DT::datatable()] columnDefs entry needs.

Deprecations and Removals

• Removed the manual.colors argument from dittoViz_scatterPlotServer(). It was the only module with such an argument, and it hard-overrode the color picker, so the colors it supplied could not be edited. Pass the same named vector as defaults = list(color.panel = ...) instead, which every module now understands and which leaves the colors editable.

Bug Fixes

• Corrected two documentation errors that would mislead anyone following the vignettes. quick-start, defaults-and-hiding, and custom-modules all used defaults = list(main = ...) as the worked example for reactive defaults, but no module exposes a plot title: every server passes main = NULL and none reads input$main, so the example was a silent no-op. The examples now use color.by, which modules do read, and the reactive-defaults sections note that an unrecognised key is silently ignored by get_default(). Separately, custom-modules’ “Hiding Base Module Inputs” example passed hide.inputs to *InputsUI(); that argument belongs to *Server(), and since no *InputsUI() accepts ... the example failed with an unused-argument error. Found while benchmarking agent skills against the docs (#341).

• Every module server (and dataFilterServer()) now requires its data reactive to yield a data frame: values that are not data frames are coerced with as.data.frame(), and a NULL makes the module wait for data rather than error. A parent app that briefly emits NULL can no longer take a plot down with it.

• Fixed modules rendering their plot two or three times for a single change (somewhat related to #325). Several modules compute a value on the server and push it into one of their own inputs with update*Input(), which is an asynchronous client round-trip: the plot rendered once with the stale value and again when the client echoed the new one. On load dittoViz_yPlot did this three times over (y-axis range, stat comparison pairs, and the rebuilt multiColorPicker).

  • These inputs are now wrapped in freezeReactiveValue() so dependents pause until the new value lands, giving a single render. This still applies to stat.pairs (dittoViz_yPlot, plotthis_BoxPlot, plotthis_ViolinPlot) and facet.scale (plotthis_BoxPlot). The colour picker and the y-axis range were handled this way too at first; both have since moved to a server-side store, for the reasons in the #338 entry below.
  • Added a section in the “Adding a New Module” vignette describing this pattern.

• Fixed an initialization bug in multiColorPicker() due to string indexing rather than position, leading to out of bounds errors when a group label was an empty string.

• Fixed the dittoViz_yPlot module re-rendering its plot when the user merely switched to the Data tab. The colour picker is built by a renderUI() on that tab, and Shiny suspends an output whose tab is hidden, so a change to the palette’s groups (setting “Multivar Aesthetic” to “color”, say, which keys the palette by variable name) could not rebuild the picker when it happened. The rebuild waited for the tab to be opened, and the value it reported then re-rendered the plot for what was only a tab click. The plot now depends on the resolved palette, the group-to-colour mapping it actually draws with (held in a reactiveVal()), rather than on the picker’s raw value. A rebuilt picker re-seeded from that same resolution therefore changes nothing to re-render for, while a colour the user actually picks comes straight through.

• Fixed every module that uses multiColorPicker() rendering its plot an extra time on initialization, and again the first time the user opened the tab the picker lives on (#338). The plot depended on the picker’s raw input$<key>, which is NULL until the browser binds the widget and reports back — so the echo of a mapping the server had just seeded the picker with still counted as a change and rebuilt the plot. An attempt utilizing freezeReactiveValue() to guard didn’t work: inside a renderUI() it pauses only the readers that run after it in the same flush, and at startup the plot output runs first, so the freeze landed too late to pause anything.

  • Every module now reads a server-resolved palette instead. The new exported helper setup_group_colors() resolves the group-to-colour mapping as soon as the group set is known and holds it in a reactiveVal(), which only invalidates on a real change. A rebuilt picker echoing the palette already in use costs nothing, while a colour the user picks comes straight through. piePlot and radarPlot, which had no guard at all, are covered for the first time.
  • The picker’s palette dropdown no longer carries an HTML id. Shiny’s select binding claims every <select> with one, so each picker was quietly registering a stray input[["<inputId>-palette"]] alongside its own value. The widget’s JavaScript and CSS both find that element by class, so nothing needed the id.
  • The “Adding a New Module” vignette’s “Updating Your Own Inputs From the Server” section now documents this pattern for renderUI()-rebuilt widgets.

• The y-axis limits now leave more room for significance brackets, and no longer cost an extra render on the way in. The plot read the raw input$y.min/input$y.max, which the module had just pushed to the browser, so their echo rebuilt it — the same freezeReactiveValue() that could not cover the colour picker was covering these no better.

  • dittoViz_yPlot, plotthis_BoxPlot, plotthis_BarPlot and plotthis_ViolinPlot now read a server-side store, the new exported setup_axis_range(), so the echo of a limit the module itself set changes nothing while a limit the user types comes straight through. Startup drops a render in each.
  • Brackets are stacked above the data, and nothing had reserved room for them: the axis was silently rescaled at draw time to whatever they needed. Worse, that rescale was applied as an assignment rather than a maximum, so enabling statistics shrank a y-axis maximum the user had deliberately set — a plot limited to 0-20 was pulled back to the top of the brackets. apply_stat_annotations() gains a y.max argument and now only ever raises the top, never lowers it.
  • The new exported stat_bracket_y_max() works out how high the brackets will reach, and the three modules that draw them reserve that room up front, so the y.max control shows the limit actually in use. It shares the bracket packing with the drawing code, so the two agree exactly, and it honours hide.ns (on by default) rather than reserving room for brackets that are never drawn.

• Fixed the dittoViz_yPlot reset button calling updateCheckboxGroupInput() on its “Plots” select, so resetting left the plot type selection untouched.

• Fixed axis titles not reflecting applied data adjustments in the dittoViz_yPlot, dittoViz_scatterPlot, and linePlot modules (#321). The annotation-persistence feature added in 0.3.0 was re-applying the previously captured title text on every rebuild, clobbering the freshly generated adjustment-aware label (e.g. log2(units)). Axis titles carrying an active adjustment are now always regenerated, while a manually edited title with no adjustment still persists and the dragged title position persists in all cases. finalize_manual_edits() gains a regen_keys argument to drive this. Axis titles are also regenerated (rather than persisted) when the plotted variable for that axis changes, via the new exported helper reset_axis_title_text(), since a manual title only makes sense for the variable it was written for. Shared axis titles in faceted linePlot/dumbbellPlot figures (built via build_facet_annotations()) are now tagged as axis annotations so their dragged position survives label changes; as a result they now pick up the axis-title font settings rather than the facet-title font settings.

• The main plot title is now blank by default in the dittoViz_yPlot and dittoViz_scatterPlot modules (previously dittoViz’s main = "make" auto-generated a title from the variable name and regenerated it on every re-render). Users can still add a title interactively by editing it on the plot.

VizModules 0.3.0

CRAN release: 2026-07-27

New Modules

• Turned the Figure Builder into a reusable, namespaced Shiny module (figureBuilderUI() / figureBuilderServer()), so it can be embedded inside a larger app and instantiated more than once, just like the plot modules. figureBuilderApp() is now a thin wrapper around this module and keeps its existing behaviour. The canvas CSS/JS was made namespace-safe (class-based, per-instance) so multiple builders can coexist on one page.
  • Panel labels (a, b, c …) now render live on the canvas as soon as they are chosen from the “Panel labels” menu (and renumber as panels are added, removed, or dragged), instead of only appearing in the exported SVG.
  • Moved the Figure Builder app into an exported figureBuilderApp() function so it can be launched directly (figureBuilderApp()), seeded with custom datasets via data_list, extended with custom modules via module_registry, and returned either as a shinyApp() object or as separate ui/server components (return_components = TRUE). The bundled inst/apps/figure-builder app is now a thin wrapper around this function.
  • Added to Gallery App.

Improved/New Functionality

• Facet/split selectors across all modules now only offer valid faceting variables. Faceting (or splitting) is restricted to categorical columns (character or factor) with fewer than 50 unique values; numeric columns and high-cardinality categoricals are no longer selectable, preventing accidental creation of an unwieldy number of panels. This is powered by a new internal helper, .facet_check(), whose output populates the facet/split input choices.
• Simplified boxplot outlier hiding to rely on native plotly boxpoints = FALSE behaviour (via ggplot2’s outlier.shape = NA in the plotthis_BoxPlot module and dittoViz::yPlot’s boxplot.show.outliers argument in dittoViz_yPlot), rather than post-hoc marker manipulation. Removed the now-unused internal helper .remove_boxplot_outliers(). This is more robust with plotly 4.12.0+.
• Added a new reusable custom Shiny input, multiDynamicInput() (with updateMultiDynamicInput()), that lets users dynamically add and remove rows of heterogeneous inputs. Each row is described by a generic row_spec (a named list of field specs using either a type alias — select, text, numeric, slider, checkbox, colour — or an arbitrary input constructor via fn), a + Add button appends rows, each row has an X delete button, and fields wrap to a new line after max_per_row (default 4). The value returned to the server is a named list of rows (model1, model2, …), each a named list keyed by the field names. Add/delete are handled client-side, and values are read back generically via each field’s registered Shiny input binding, so any input type is supported.
  • Added vignette vignette("using-custom-shiny-inputs") documenting multiDynamicInput() usage: row_spec definition, pre-filling with elements, reading values, and server-side updates.
• Added generic modeling capabilities to dittoViz_scatterPlot module. The module’s custom-model feature now supports multiple models at once via multiDynamicInput(): add as many rows as you like, each with its own model type (lm/glm/loess/nls), formula, line colour, and line width, and every valid model is fitted against the active (filtered) data and overlaid as its own line (respecting faceting). Formulas are validated by the internal .safe_build_model() helper to ensure safety.
  • This includes the ability to add custom model backends via register_model_backend(), get_model_backend(), list_model_backends(), and build_model_row_spec(). Backends declare a fit function, a predict function, validated output classes, and optional extra UI fields that appear/hide dynamically based on the selected model type. The four built-in backends (lm, glm, loess, nls) are registered automatically at package load. Extra UI fields from backends are forwarded to fit() via ....
  • Added vignette vignette("custom-model-lines") documenting the model backend registry: how the pipeline works, setting model defaults, registering custom backends (with drc and mgcv examples), and how extra fields flow through to the fit function.
• Pass defaults, hide.inputs, and hide.tabs arguments to the module app factory functions in all module app wrappers, so that users can pre-fill or hide controls when testing modules in isolation.
• More intelligent input hiding logic so that when individual inputs are hidden (via hide.inputs or dynamically in response to other inputs), the remaining controls reflow to fill the space and no empty gaps are left in the UI. Input grids are now laid out with a wrapping flexbox container via organize_inputs(). Optional elements are handled gracefully.
• Added continuous color-scale trimming controls (“Lower Quantile”, “Upper Quantile”, “Lower Cutoff”, and “Upper Cutoff”) to the plotthis_DotPlot, plotthis_BarPlot, and plotthis_SplitBarPlot modules, exposing the new lower_quantile/upper_quantile/lower_cutoff/upper_cutoff arguments from plotthis 0.13.0. These controls appear only when the selected fill column is numeric.
• Added dot border controls (“Border Color” and “Border Size”) to the plotthis_DotPlot module, exposing the new border_color and border_size arguments from plotthis 0.13.0. border_color is limited to a single constant color in the module UI.
• Updated the plotthis_DotPlot “Fill Cutoff” control to pair a numeric value with a new “Fill Cutoff Direction” selector (<, <=, >, >=), matching plotthis 0.13.0’s string-expression fill_cutoff (e.g. "< 18").
• Added annotation persistence, i.e. annotation positions persist when the plot is re-rendered. This extends to axis/facet titles and custom annotations, which means much less finagling during iterative editing.

Bug Fixes

• Fixed broken input hiding when using hide.inputs and hide.tabs arguments in module app wrappers due to lazy UI injection via renderUI, which effectively overwrote the hide calls. renderUI also re-renders the input UIs every time a dataset changes - now if the dataset changes, the inputs are re-rendered but the hide calls are re-applied to maintain the hidden state.
• Fixed an error in plotthis_SplitBarPlot where the categorical text position input was not respected if the axes were flipped. Now the text position input is respected regardless of axis orientation.
• Export numerous internal helper functions for use in custom modules, particularly those related to axes, faceting, and layouts. It became apparent these were necessary as initial work began on sciVizModules.
• Fixed a bug in dittoViz_yPlot where plot selection and outlier hiding were not respected appropriately due to a typo in the boxplot.show.outliers input name.
• Fixed a bug in dittoViz_scatterPlot where 2 split.by inputs caused an error due to improper checks for empty strings on a vector of elements.
• Fixed a bug in dittoViz_scatterPlot where highlight aesthetics weren’t applied when a categorical x-axis was used.

Deprecations and Removals

• Removed ternaryPlot module, as it is just a bad plot that’s impossible to actually interpret or really utilize effectively.

VizModules 0.2.0

CRAN release: 2026-06-16

• Created the Figure Builder app so that users can dynamically construct multi-panel figures using different data sets and plot types on a single page. Allows for full page SVG export, source data dump organized per panel, and full customization of plot position and size.
• All *OutputUI() functions gained a resizable argument (default TRUE). When FALSE, the plot output is no longer wrapped in shinyjqui::jqui_resizable(), which avoids a redundant resize handle when the output is embedded in a container that already provides resizing (such as the Figure Builder app cards).
• Added a new plotthis_DotPlot module (plotthis_DotPlotInputsUI(), plotthis_DotPlotOutputUI(), plotthis_DotPlotServer(), and the plotthis_DotPlotApp() convenience wrapper) that wraps plotthis::DotPlot() for interactive dot plots, including a custom dot-size legend since plotly still lacks that capability.
• Added the example_markers dataset, a simulated single-cell marker-gene expression table (immune cell types × marker genes) used as the default example data for the DotPlot module.
• Added “Source Data” download button at the bottom of every module’s control panel. The button creates and downloads a ZIP file containing a self-contained HTML of the plotly plot, a CSV of the plot data (retrieved via plotly::plotly_data()), and for modules with statistics enabled (Box / Violin / yPlot), a table of the statistics info. Source downloads are now built from the exported collect_source_data() and create_source_download_handler() helpers, and each module server returns its source reactive so it can be reused (e.g. by the Figure Builder). Given source data is now required by many journals, this is important.
• Removed old interactive plot download button and associated helper function.
• Removed old dynamically hidden stats download button and associated logic, since stats are now included in the source download when applicable.
• Statistic helper functions are now exported allowing users to annotate plotly graphs with custom statistics: compute_pairwise_stats(), create_stat_annotations(), apply_stat_annotations(), generate_pair_strings(), and parse_pair_strings().
• Exposed empty_plot() for use as a placeholder, e.g. if parameters aren’t valid for a given plot type, to pass that info to user without ugly error messages.
• Faceting improvements - new internal helpers that control subplot spacing, subplot size, and facet_scale handling. This fixes much of the wonkiness for plots with many panels. Uniform inputs added for panel spacing across all modules.
• Axis titles now uniformly added as annotations to allow interactive repositioning.
• Condensed package wide workflows with simple helpers, e.g. apply_title_layout(), resulting in significantly less jank.
• Axis adjustments are now properly reflected in axis/legend titles for appropriate modules, e.g. yPlot, scatterPlot, linePlot.
• Removed a handful of spurious/non-functional inputs, particularly for the dittoViz_scatterPlot module.
• Custom size.by legends added for plotthis_DotPlot and dittoViz_scatterPlot modules, since plotly does not yet support these.
• Update docstrings to reflect new inputs and features and clarify which parameters of underlying plotting functions may not be implemented.
• Various border fixes for faceted plots.

VizModules 0.1.1

CRAN release: 2026-04-08

• Minor DESCRIPTION and doc fixes for CRAN compliance.

VizModules 0.1.0

• Submitted to CRAN.