Merge branch 'v2024.03.29' of github.com:ACCLAB/DABEST-python into JAnns98-patch-1

# Conflicts:
#	nbs/tutorials/07-forest_plot.ipynb

Author: Jacobluke-

CHANGELOG.md

@@ -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.

README.md

@@ -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.

dabest/__init__.py

@@ -3,4 +3,4 @@
 from ._effsize_objects import TwoGroupsEffectSize, PermutationTest
 from ._dabest_object import Dabest
 
-__version__ = "2023.03.29"
+__version__ = "2024.03.29"

nbs/01-getting_started.ipynb

@@ -12,6 +12,43 @@
     "- order: 1"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5b3dcdd6",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aebebc2",
+   "metadata": {},
+   "source": [
+    "DABEST is a package for **D**ata **A**nalysis with **B**ootstrapped **EST**imation\n",
+    "\n",
+    "[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a simple framework that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one’s experiment/intervention, as opposed to a false dichotomy engendered by *P* values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc075f5",
+   "metadata": {},
+   "source": [
+    "An estimation plot has two key features.\n",
+    "\n",
+    "1. It **presents all datapoints** as a swarmplot, which orders each point to display the underlying distribution.\n",
+    "2. It presents the **effect size** as a **bootstrap 95% confidence interval** on a **separate but aligned axes**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4c2e459",
+   "metadata": {},
+   "source": [
+    "DABEST powers [estimationstats.com](estimationstats.com), allowing everyone access to high-quality estimation plots."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d1d5cb1a",
@@ -27,14 +64,14 @@
    "source": [
     "\n",
     "\n",
-    "Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.6, 3.7 and 3.8.\n",
+    "Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.8 and onwards.\n",
     "\n",
     "In addition, the following packages are also required (listed with their minimal versions):\n",
     "\n",
-    "* [numpy 1.22.4](https://www.numpy.org)\n",
+    "* [numpy 1.23.5](https://www.numpy.org)\n",
     "* [scipy 1.9.3](https://www.scipy.org)\n",
     "* [matplotlib 3.6.3](https://www.matplotlib.org)\n",
-    "* [pandas 1.5.3](https://pandas.pydata.org)\n",
+    "* [pandas 1.5.0](https://pandas.pydata.org)\n",
     "* [seaborn 0.12.2](https://seaborn.pydata.org)\n",
     "* [lqrt 0.3.3](https://github.com/alyakin314/lqrt)\n",
     "\n",

nbs/02-about.ipynb

@@ -17,7 +17,9 @@
     "\n",
     "DABEST is written in Python by [Joses W. Ho](https://twitter.com/jacuzzijo), with design and input from [Adam Claridge-Chang](https://twitter.com/adamcchang) and other [lab members](https://www.claridgechang.net/people.html).\n",
     "\n",
-    "Additional features in v2023.02.14 were added by [Yixuan Li](https://github.com/LI-Yixuan), [Zinan Lu](https://github.com/Jacobluke-) and [Rou Zhang](https://github.com/ZHANGROU-99).\n",
+    "Features in v2024.03.29 were added by [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Ana Rosa Castillo](https://github.com/cyberosa).\n",
+    "\n",
+    "Features in v2023.02.14 were added by [Yixuan Li](https://github.com/LI-Yixuan), [Zinan Lu](https://github.com/Jacobluke-) and [Rou Zhang](https://github.com/ZHANGROU-99).\n",
     "\n",
     "To find out more about the authors' research, please visit the [Claridge-Chang lab webpage](http://www.claridgechang.net/).\n",
     "\n",
@@ -81,14 +83,6 @@
     "POSSIBILITY OF SUCH DAMAGE.\n",
     "</div>\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de35a697",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {