CHANGELOG.md

Release notes

v2025.10.20

New Features

1. Whorlmap Visualization: Introducing a new way to visualize effect sizes from multiple comparisons in a grid of whorled square cells. This design condenses information from full bootstrap distributions of an array of contrast objects into a compact visual representation. It optimizes visual real estate by presenting a clear impression of the whole dataset at a glance while retaining nuanced distributional information for further scrutiny. Whorlmaps are a space-efficient alternative to stacked forest plots when working with multi-dimensional DABEST objects from large scale experiments.

2. Slopegraphs have a new look: Slopegraphs for paired continuous data now show summary statistics for each group. By default, a thick trend line connects group means, with vertical bars indicating standard deviation. Users can choose the summary type via the group_summaries argument in .plot() — options include 'mean_sd', 'median_quartiles', or None. Appearance can be customized using group_summaries_kwargs. See the group summaries section in the Plot Aesthetics tutorial for more details.

3. Fixed Mini-meta Weighted Delta Calculation: The weighted delta in mini-meta plots has been updated to ensure accurate calculation and reporting of the weighted delta.

4. Expanded custom_palette functionality:

   • Barplots (unpaired, proportional): The custom_palette dict can now take 0 and 1 as keys to color the filled and unfilled portions of the plots. See the custom palette section in the Plot Aesthetics tutorial for more details.

   • Slopegraphs (paired, non proportional): The custom_palette can now be used to color the contrast bars and effect-size curves. See the custom palette section in the Plot Aesthetics tutorial for more details.

v2025.03.27

New Features

1. Python 3.13 Support: DABEST now supports Python 3.10—3.13.

2. Horizontal Plots: Users can now create horizontal layout plots, providing compact data visualization. This can be achieved by setting horizontal=True in the .plot() method.

3. Forest Plots: Forest plots provide a simple and intuitive way to visualize many delta-delta (or delta g), mini-meta, or regular delta effect sizes at once from multiple different dabest objects without presenting the raw data.

4. Gridkey: Users can now represent experimental labels in a ‘gridkey’ table. This can be accessed with the gridkey parameter in the .plot() method.

5. Other Visualization Improvements:

   • Comparing means and effect sizes: The estimation plots now include three types of customizable visual features to enhance contextualization and comparison of means and effect sizes:

     • Bars for the mean of the observed values (raw_bars): Colored rectangles that extend from the zero line to the mean of each group's raw data. These bars visually highlight the central tendency of the raw data.

     • Bars for effect size/s (contrast_bars): Similar to raw bars, these highlight the effect-size difference between two groups (typically test and control) in the contrast axis. They provide a visual representation of the differences between groups.

     • Summary bands (reference_band): An optional band or ribbon that can be added to emphasize a specific effect size’s confidence interval that is used as a reference range across the entire contrast axis. Unlike raw and contrast bars, these span horizontally (or vertically if horizontal=True) and are not displayed by default.

       Raw and contrast bars are shown by default. Users can customize these bars and add summary bands as needed.

   • Tighter spacing in delta-delta and mini-meta plots: We have adjusted the spacing of delta-delta and mini-meta plots to reduce whitespace. The new format brings the overall effect size closer to the two-groups effect sizes. In addition, delta-delta plots now have a gap in the zero line to separate the delta-delta from the ∆ effect sizes.

   • Delta-delta effect sizes for proportion plots: In addition to continuous data, delta-delta plots now support binary data (proportions). This means that 2-way designs for binary outcomes can be analyzed with DABEST.

   • Proportion plots sample sizes: The sample size of each binary option for each group can now be displayed. These can be toggled on/off via the prop_sample_counts parameter.

   • Effect size lines for paired plots: Along with lines connecting paired observed values, the paired plots now also display lines linking the effect sizes within a group in the contrast axes. These lines can be toggled on/off via the contrast_paired_lines parameter.

   • Baseline error curves: To represent the baseline/control group in the contrast axes, it is now possible to plot the baseline dot and the baseline error curve. The dot is shown by default, while the curve can be toggled on/off via the show_baseline_ec parameter. This dot helps make it clear where the baseline comes from i.e. the control minus itself. The baseline error curve can be used to show that the baseline itself is an estimate inferred from the observed values of the control data.

   • Delta text: Effect-size deltas (e.g. mean differences) are now displayed as numerals next to their respective effect size. This can be toggled on/off via the delta_text parameter.

   • Empty circle color palette: A new swarmplot color palette modification is available for unpaired plots via the empty_circle parameter in the .plot() method. This option modifies the two-group swarmplots to have empty circles for the control group and filled circles for the experimental group.

6. Miscellaneous Improvements & Adjustments

   • Numba for speed improvements: We have added Numba to speed up the various calculations in DABEST. Precalculations will be performed during import, which will help speed up the subsequent loading and plotting of data.

   • Terminology/naming updates: During the refactoring of the code, we have made several updates to the documentation and terminology to improve clarity and consistency. For example:

     • Plot arguments have been adjusted to bring more clarity and consistency in naming. Arguments relating to the rawdata plot axis will now be typically referred to with raw while arguments relating to the contrast axis will be referred to with contrast. For example, raw_label replaces swarm_label and bar_label. The various kwargs relating to each different type of plot (e.g., swarmplot_kwargs) remain unchanged.

     • The method to utilise the Delta g effect size is now via the .hedges_g.plot() method rather than creating a whole new Delta_g object as before. The functionality remains the same, it plots hedges_g effect sizes and then the Delta g effect size alongside these (if a delta-delta experiment was loaded correctly).

   • Updated tutorial pages: We have updated the tutorial pages to reflect the new features and changes. The tutorial pages are now more comprehensive and (hopefully!) more intuitive!

   • Results dataframe for delta-delta and mini-meta plots: A results dataframe can now be extracted for both the delta-delta and mini-meta effect size data (similar to the results dataframe for the regular effect sizes). These can be found via the .results attribute of the .delta_delta or .mini_meta object.

v2024.03.29

New Features

• Standardized Delta-delta Effect Size: We added a new metric akin to a Hedges’ g for delta-delta effect size, which allows comparisons between delta-delta effects generated from metrics with different units.

• New Paired Proportion Plot: This feature builds upon the existing proportional analysis capabilities by introducing advanced aesthetics and clearer visualization of changes in proportions between different groups, inspired by the informative nature of Sankey Diagrams. It's particularly useful for studies that require detailed examination of how proportions shift in paired observations.

• Customizable Swarm Plot: Enhancements allow for tailored swarm plot aesthetics, notably the adjustment of swarm sides to produce asymmetric swarm plots. This customization enhances data representation, making visual distinctions more pronounced and interpretations clearer.

Enhancement

• Miscellaneous Improvements: This version also encompasses a broad range of miscellaneous enhancements, including bug fixes, Bootstrapping speed improvements, new templates for raising issues, and updated unit tests. These improvements are designed to streamline the user experience, increase the software's stability, and expand its versatility. By addressing user feedback and identified issues, DABEST continues to refine its functionality and reliability.

v2023.03.29

New Features

• Repeated measures: Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis.

• Proportional data: Generates proportional bar plots, proportional differences, and calculates Cohen’s h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer’s exact test.

• The ∆∆ plot: Calculates the delta-delta (∆∆) for 2 × 2 experimental designs and plots the four groups with their relevant effect sizes. This design can be used as a replacement for the 2 × 2 ANOVA.

• Mini-meta: Calculates and plots a weighted delta (∆) for meta-analysis of experimental replicates. Useful for summarizing data from multiple replicated experiments, for example by different scientists in the same lab, or the same scientist at different times. When the observed values are known (and share a common metric), this makes meta-analysis available as a routinely accessible tool.