Dabest

New Features

- **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.

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

- **Gridkey**: Users can now represent their experimental labels in a gridkey format. This can be accessed with the `gridkey` argument in the `plot()` method.

- **Aesthetic Updates**: We have made several aesthetic improvements to the plots, including:
- **Raw, Contrast, and Summary bars**: We added bars highlighting the various groups' differences. These bars can be customized to suit the user’s needs. Raw and contrast bars are provided by default, summary bars can be added by the user.

- **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 added effect size closer to the regular effect sizes. In addition, delta-delta plots now have a gap in the zeroline to separate the delta-delta and regular effect sizes.

- **Delta-delta Effect Sizes for Proportion Plots**: Delta-delta experimental plotting now supports binary data.

- **Proportion Plots Sample Sizes**: The sample size of each binary option for each group can now be displayed. These can be toggled on or off via the `prop_sample_counts` parameter.

- **Effect Size Lines for Paired Plots**: Paired plots now display lines linking the effect sizes within a group together in the contrast axes. These can be toggled on or off via the `contrast_paired_lines` parameter.

- **Baseline Error Curves**: Baseline error dot and curve are now available to represent the baseline/control group in the contrast axes. The dot is shown by default, while the curve can be toggled on/off by the user (via the `show_baseline_ec` parameter).

- **Delta Text**: Effect size deltas are now displayed as text next to their respective effect size. This can be toggled on or 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.

Enhancement

- **Python 3.13 Support**: DABEST now supports Python 3.10-3.13.

- **Numba for Speed Improvements**: We have included Numba to speed up the various calculations in DABEST. This should make the calculations faster and more efficient. Importing DABEST may take a little longer than before, and a progress bar will appear during the import process to show the calculations being performed. Once imported, loading and plotting data should now be faster.

- **Terminology Updates**: 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 Deltas’ 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 Deltas’ 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.

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.

This release is a complete rebuild of dabestr.

Previous functions from v0.3.0 and before are now depreciated.

Main API
This version of dabestr features a new Main API. The following functions have been sequentially organised for their intended procedural utilisation.

* `load()`
processes and converts a tidy dataset into the dabestr format.

* `mean_diff()`, `median_diff()`, `cohens_d()`, `hedges_g()`, `cliffs_delta()`, `cohens_h()`
Computes the effect size for each control-test group pairing in `idx`.

* `dabest_plot()` produces a Gardner-Altman estimation plot or a Cumming estimation plot depending on whether float_contrast is TRUE.

New features
* Plotting of shared control and repeated measures
* Proportion plots (unpaired and paired)
* Mini-Meta Delta plots
* Delta-Delta plots

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.

* This is a breaking release that includes standardised effect sizes, and a bunch of bugfixes.

* This release implements the ability to supply a custom vector of colors to `palette` during plotting. There is also a bugfix that misreported the Ns for the test and control group.