ACCLAB
diff --git a/‎CHANGELOG.md‎
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diff --git a/‎README.md‎
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diff --git a/‎dabest/__init__.py‎
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diff --git a/‎dabest/_modidx.py‎
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diff --git a/‎dabest/forest_plot.py‎
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@@ -2,13 +2,29 @@
 
 <!-- do not remove -->
 
-## 2023.03.29
+## v2024.03.29
 
 ### New Features
-- Add new form of paired proportion plots for a better support of Repeated Measures
 
+- **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. 
 
-## 0.2.3
+- **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.
 
-### Bug Fixes
-- Fixes a bug that jammed up when the xvar column was already a pandas Categorical. Now we check for this and act appropriately.
+- **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.
@@ -2,34 +2,48 @@
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
+[![minimal Python
+version](https://img.shields.io/badge/Python%3E%3D-3.8-6666ff.svg)](https://www.anaconda.com/distribution/)
+[![PyPI
+version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)
+[![Downloads](https://img.shields.io/pepy/dt/dabest.svg)](https://pepy.tech/project/dabest)
+[![Free-to-view
+citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://rdcu.be/bHhJ4)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause--Clear-orange.svg)](https://spdx.org/licenses/BSD-3-Clause-Clear.html)
+
 ## Recent Version Update
 
-On 20 March 2023, we officially released **DABEST v2023.02.14 for
-Python**. This new version provided the following new features:
-
-1.  **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.
-
-2.  **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 Fisher’s exact test.
-
-3.  **The $\Delta\Delta$ plot.** Calculates the delta-delta
-    ($\Delta\Delta$) 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.
-
-4.  **Mini-meta.** Calculates and plots a weighted delta ($\Delta$) 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.
+We are proud to announce **DABEST Version Ondeh (v2024.03.29)**. This
+new version of the DABEST Python library provides several new features
+and includes performance improvements.
+
+1.  **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.
+
+2.  **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.
+
+3.  **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.
+
+4.  **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.
 
 ## Contents
 
@@ -77,7 +91,7 @@ allowing everyone access to high-quality estimation plots.
 
 ## Installation
 
-This package is tested on Python 3.6, 3.7, 3.8 and 3.10. It is highly
+This package is tested on Python 3.8 and onwards. It is highly
 recommended to download the [Anaconda
 distribution](https://www.continuum.io/downloads) of Python in order to
 obtain the dependencies easily.
@@ -86,13 +100,6 @@ You can install this package via `pip`.
 
 To install, at the command line run
 
-``` shell
-conda config --add channels conda-forge
-conda install dabest
-```
-
-or –\>
-
 ``` shell
 pip install dabest
 ```
@@ -128,8 +135,8 @@ iris_dabest.mean_diff.plot();
 dataset](iris.png)
 
 Please refer to the official
-[tutorial](https://acclab.github.io/DABEST-python-docs/tutorial.html)
-for more useful code snippets.
+[tutorial](https://acclab.github.io/DABEST-python/) for more useful code
+snippets.
 
 ## How to cite
 
@@ -147,8 +154,8 @@ PDF](https://rdcu.be/bHhJ4)
 
 ## Bugs
 
-Please report any bugs on the [Github issue
-tracker](https://github.com/ACCLAB/DABEST-python/issues/new).
+Please report any bugs on the [issue
+page](https://github.com/ACCLAB/DABEST-python/issues/new).
 
 ## Contributing
 
@@ -184,7 +191,7 @@ To test DABEST, you need to install
 - Run `pytest` in the root directory of the source distribution. This
   runs the test suite in the folder `dabest/tests/mpl_image_tests`.
 - Run `nbdev_test` in the root directory of the source distribution.
-  This runs the value assertion tests in parent folder `dabest/tests`
+  This runs the value assertion tests in the folder `dabest/tests`
 
 The test suite ensures that the bootstrapping functions and the plotting
 functions perform as expected.
 
@@ -3,4 +3,4 @@
 from ._effsize_objects import TwoGroupsEffectSize, PermutationTest
 from ._dabest_object import Dabest
 
-__version__ = "2023.03.29"
+__version__ = "2024.03.29"
@@ -64,7 +64,9 @@
             'dabest.forest_plot': { 'dabest.forest_plot.extract_plot_data': ( 'API/forest_plot.html#extract_plot_data',
                                                                               'dabest/forest_plot.py'),
                                     'dabest.forest_plot.forest_plot': ('API/forest_plot.html#forest_plot', 'dabest/forest_plot.py'),
-                                    'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py')},
+                                    'dabest.forest_plot.load_plot_data': ('API/forest_plot.html#load_plot_data', 'dabest/forest_plot.py'),
+                                    'dabest.forest_plot.map_effect_attribute': ( 'API/forest_plot.html#map_effect_attribute',
+                                                                                 'dabest/forest_plot.py')},
             'dabest.misc_tools': { 'dabest.misc_tools.get_varname': ('API/misc_tools.html#get_varname', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.merge_two_dicts': ('API/misc_tools.html#merge_two_dicts', 'dabest/misc_tools.py'),
                                    'dabest.misc_tools.print_greeting': ('API/misc_tools.html#print_greeting', 'dabest/misc_tools.py'),
 
@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/API/forest_plot.ipynb.
 
 # %% auto 0
-__all__ = ['load_plot_data', 'extract_plot_data', 'forest_plot']
+__all__ = ['load_plot_data', 'extract_plot_data', 'map_effect_attribute', 'forest_plot']
 
 # %% ../nbs/API/forest_plot.ipynb 5
 import matplotlib.pyplot as plt
@@ -72,28 +72,42 @@ def extract_plot_data(contrast_plot_data, contrast_type):
 
     return bootstraps, differences, bcalows, bcahighs
 
+def map_effect_attribute(attribute_key):
+    # Check if the attribute key exists in the dictionary
+    effect_attr_map = {
+        "mean_diff": "Mean Difference",
+        "median_diff": "Median Difference",
+        "cliffs_delta": "Cliffs Delta",
+        "cohens_d": "Cohens d",
+        "hedges_g": "Hedges g",
+        "delta_g": "Delta g"
+    }
+    if attribute_key in effect_attr_map:
+        return effect_attr_map[attribute_key]
+    else:
+        raise TypeError("The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`,`median_diff`,`cliffs_delta`,`cohens_d``, and `hedges_g`.")  # Return a default value or message if the key is not found
 
 def forest_plot(
     contrasts: List,
     selected_indices: Optional[List] = None,
     contrast_type: str = "delta2",
-    xticklabels: Optional[List] = None,
     effect_size: str = "mean_diff",
     contrast_labels: List[str] = None,
-    ylabel: str = "value",
+    ylabel: str = "effect size",
     plot_elements_to_extract: Optional[List] = None,
     title: str = "ΔΔ Forest",
     custom_palette: Optional[Union[dict, list, str]] = None,
-    fontsize: int = 20,
+    fontsize: int = 12,
+    title_font_size: int =16,
     violin_kwargs: Optional[dict] = None,
     marker_size: int = 20,
     ci_line_width: float = 2.5,
-    zero_line_width: int = 1,
+    desat_violin: float = 1,
     remove_spines: bool = True,
     ax: Optional[plt.Axes] = None,
     additional_plotting_kwargs: Optional[dict] = None,
     rotation_for_xlabels: int = 45,
-    alpha_violin_plot: float = 0.4,
+    alpha_violin_plot: float = 0.8,
     horizontal: bool = False  # New argument for horizontal orientation
 )-> plt.Figure:
     """  
@@ -106,11 +120,9 @@ def forest_plot(
     selected_indices : Optional[List], default=None
         Indices of specific contrasts to plot, if not plotting all.
     analysis_type : str
-         the type of analysis (e.g., 'delta2', 'minimeta').
-    xticklabels : Optional[List], default=None
-        Custom labels for the x-axis ticks.
+         the type of analysis (e.g., 'delta2', 'mini_meta').
     effect_size : str
-        Type of effect size to plot (e.g., 'mean_diff', 'median_diff').
+        Type of effect size to plot (e.g., 'mean_diff', 'median_diff', `cliffs_delta`,`cohens_d``, and `hedges_g`).
     contrast_labels : List[str]
         Labels for each contrast.
     ylabel : str
@@ -125,14 +137,14 @@ def forest_plot(
         Custom color palette for the plot.
     fontsize : int
         Font size for text elements in the plot.
+    title_font_size: int =16
+        Font size for text of plot title.
     violin_kwargs : Optional[dict], default=None
         Additional arguments for violin plot customization.
     marker_size : int
         Marker size for plotting mean differences or effect sizes.
     ci_line_width : float
         Width of confidence interval lines.
-    zero_line_width : int
-        Width of the line indicating zero effect size.
     remove_spines : bool, default=False
         If True, removes top and right plot spines.
     ax : Optional[plt.Axes], default=None
@@ -161,14 +173,13 @@ def forest_plot(
     if selected_indices is not None and not isinstance(selected_indices, (list, type(None))):
         raise TypeError("The `selected_indices` must be a list of integers or `None`.")
 
+    # For the 'contrast_type' parameter
     if not isinstance(contrast_type, str):
-        raise TypeError("The `contrast_type` argument must be a string.")
-    
-    if xticklabels is not None and not all(isinstance(label, str) for label in xticklabels):
-        raise TypeError("The `xticklabels` must be a list of strings or `None`.")
-    
+        raise TypeError("The `contrast_type` argument must be a string. Please choose from `delta2` and `mini_meta`.")
+
+    # For the 'effect_size' parameter
     if not isinstance(effect_size, str):
-        raise TypeError("The `effect_size` argument must be a string.")
+        raise TypeError("The `effect_size` argument must be a string. Please choose from the following effect sizes: `mean_diff`, `median_diff`, `cliffs_delta`, `cohens_d`, and `hedges_g`.")
 
     if contrast_labels is not None and not all(isinstance(label, str) for label in contrast_labels):
         raise TypeError("The `contrast_labels` must be a list of strings or `None`.")
@@ -191,9 +202,6 @@ def forest_plot(
     if not isinstance(ci_line_width, (int, float)) or ci_line_width <= 0:
         raise TypeError("`ci_line_width` must be a positive integer or float.")
 
-    if not isinstance(zero_line_width, (int, float)) or zero_line_width <= 0:
-        raise TypeError("`zero_line_width` must be a positive integer or float.")
-    
     if not isinstance(remove_spines, bool):
         raise TypeError("`remove_spines` must be a boolean value.")
 
@@ -209,6 +217,8 @@ def forest_plot(
     if not isinstance(horizontal, bool):
         raise TypeError("`horizontal` must be a boolean value.")
 
+    if (effect_size and isinstance(effect_size, str)):
+        ylabel = map_effect_attribute(effect_size)
     # Load plot data
     contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)
 
@@ -250,7 +260,7 @@ def forest_plot(
     if custom_palette:
         if isinstance(custom_palette, dict):
             violin_colors = [
-                custom_palette.get(c, sns.color_palette()[0]) for c in contrasts
+                custom_palette.get(c, sns.color_palette()[0]) for c in contrast_labels
             ]
         elif isinstance(custom_palette, list):
             violin_colors = custom_palette[: len(contrasts)]
@@ -262,12 +272,18 @@ def forest_plot(
                     f"The specified `custom_palette` {custom_palette} is not a recognized Matplotlib palette."
                 )
     else:
-        violin_colors = sns.color_palette()[: len(contrasts)]
+        violin_colors = sns.color_palette(n_colors=len(contrasts))
 
+    violin_colors = [sns.desaturate(color, desat_violin) for color in violin_colors]
+    
     for patch, color in zip(v["bodies"], violin_colors):
         patch.set_facecolor(color)
         patch.set_alpha(alpha_violin_plot)
-
+    if horizontal:
+        ax.plot([0, 0], [0, len(contrasts)+1], 'k', linewidth = 1)
+    else:
+        ax.plot([0, len(contrasts)+1], [0, 0], 'k', linewidth = 1)
+    
     # Flipping the axes for plotting based on 'horizontal'
     for k in range(1, len(contrasts) + 1):
         if horizontal:
@@ -280,19 +296,26 @@ def forest_plot(
     # Adjusting labels, ticks, and limits based on 'horizontal'
     if horizontal:
         ax.set_yticks(range(1, len(contrasts) + 1))
-        ax.set_yticklabels(contrast_labels,  rotation=rotation_for_xlabels, fontsize=fontsize)
+        ax.set_yticklabels(contrast_labels,  rotation=0, fontsize=fontsize)
         ax.set_xlabel(ylabel, fontsize=fontsize)
+        ax.set_ylim([0.7, len(contrasts) + 0.5])
     else:
         ax.set_xticks(range(1, len(contrasts) + 1))
         ax.set_xticklabels(contrast_labels, rotation=rotation_for_xlabels, fontsize=fontsize)
         ax.set_ylabel(ylabel, fontsize=fontsize)
+        ax.set_xlim([0.7, len(contrasts) + 0.5])
 
     # Setting the title and adjusting spines as before
-    ax.set_title(title, fontsize=fontsize)
+    ax.set_title(title, fontsize=title_font_size)
     if remove_spines:
-        for spine in ax.spines.values():
-            spine.set_visible(False)
-
+        if horizontal:
+            ax.spines['left'].set_visible(False)
+            ax.spines['right'].set_visible(False)
+            ax.spines['top'].set_visible(False)
+        else:
+            ax.spines['top'].set_visible(False)
+            ax.spines['bottom'].set_visible(False)
+            ax.spines['right'].set_visible(False)
     # Apply additional customizations if provided
     if additional_plotting_kwargs:
         ax.set(**additional_plotting_kwargs)