{"repo":"ACCLAB/DABEST-python","free":true,"listed":false,"github":"https://github.com/ACCLAB/DABEST-python","clone":"git clone https://github.com/ACCLAB/DABEST-python.git","description":"Data Analysis with Bootstrapped ESTimation","language":"Jupyter Notebook","stars":414,"topics":["data-visualization","data-analysis","statistics","estimation","python"],"license":"Apache-2.0","category":"analytics","readme_excerpt":"DABEST-Python Recent Version Update ✨ DABEST “Bingka” v2025.10.20 for Python is now released! ✨ Dear DABEST users, The latest version of the DABEST Python library brings new visualizations, refined plots, and improved accuracy. 1. Whorlmap 🌀: Compact visualization for multi-dimensional effects Introducing Whorlmap , a new way to visualize effect sizes from multiple comparisons in a compact, grid-based format. Whorlmaps condense information from the full bootstrap distributions of many contrast objects into a 2D heatmap-style grid of “whorled” cells . This provides an overview of the entire dataset while preserving the underlying distributional detail. They are especially useful for large-scale or multi-condition experiments, serving as a space-efficient alternative to stacked forest plots . You can generate a Whorlmap directly from multi-dimensional DABEST objects using the .whorlmap() method. See the Whorlmap tutorial for more details. 2. Slopegraphs 📈: Enhanced summaries for paired data Slopegraphs for paired continuous data now display group summary statistics . - By default, a thick trend line connects group means, with vertical bars showing standard deviation. - Choose the summary type via the group summaries argument in .plot() — options include 'mean sd' , 'median quartiles' , or None . - Customize appearance with group summaries kwargs . See the Group Summaries section in the Plot Aesthetics tutorial for more details. 3. Mini-meta Weighted Delta Fix 🧮 The weighted ","default_branch":null,"files":null,"tree":[],"storefront":"/r/ACCLAB","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ACCLAB/DABEST-python/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}