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@JAnns98 JAnns98 released this 27 Mar 07:30
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v2025.03.27
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Learn about vigilant mode.

Dear DABEST users,

DABEST "Dadar" (v2025.03.27) for Python is now released!

This new version of the DABEST Python library includes several new features and performance improvements. It’s a big one!

  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. See the Horizontal Plots tutorial for more details.

  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. See the Forest Plots
    tutorial     for more details.

  4. Gridkey: Users can now represent experimental labels in a ‘gridkey’ table. This can be accessed with the gridkey parameter
    in the .plot() method. See the gridkey section in the Plot Aesthetics tutorial for more details.

  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. For detailed
        customization instructions, please refer to the Plot Aesthetics tutorial.

    • 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.

Contributors to this update were: Jonathan Anns (@JAnns98), Zinan Lu (@Jacobluke-), Kah Seng LIAN (@sunroofgod), Yishan Mai (@maiyishan), Sangyu Xu (@sangyu), and Lucas Wang Zhuoyu (@Lucas1213WZY)

Contributors

sangyu, Jacobluke-, and 4 other contributors
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