Estimation Stats

The official twitter account for by the @adamcchang lab.

[Estimation Stats @EstimationStats]

Today we are excited to launch DABEST Version 2 aka “Ondeh” , which adds entirely new statistical graphics for paired proportions, standardized delta-delta effect size, and gives new options for dot swarms. 🧵1/6

[Estimation Stats @EstimationStats]

We hope you get a chance to use DABEST v2024.03.29 in your work. We hope to hear your feedback here and on GitHub. Refreshed documentation and tutorial can be found at https://t.co/vVg65O5U3U 🧵6/6

[Estimation Stats @EstimationStats]

Today we are excited to launch DABEST Version 2 aka “Ondeh” , which adds entirely new statistical graphics for paired proportions, standardized delta-delta effect size, and gives new options for dot swarms. 🧵1/6

[Estimation Stats @EstimationStats]

Contributors to this update were: Zinan LU (https://t.co/bObOT5OwIz), Kah Seng LIAN (https://t.co/ySuRtI7Cxg), A. Rosa Castillo (https://t.co/RoDRhTLPLy). 🧵5/6

[Estimation Stats @EstimationStats]

② Users may add a baseline error curve to shared control contrast plots, displayed below the control group. This is the distribution of the bootstrapped mean differences from comparing the control against itself and is a measure of inherent variability within the control group.

[Estimation Stats @EstimationStats]

We have also added several features not yet present in the Python version of the package:

① As you may have noticed from the images in the thread below, we have adopted asymmetrical swarmplots, thereby better showing the distribution underlying the raw data.

[Estimation Stats @EstimationStats]

Exciting news for dabestr users! We are releasing dabestr version “Lapis” (v2023.9.12), which adds estimation graphics for four new data types: repeated measures, 2 × 2 designs, proportions, and easy meta-analyses. 🧵

[Estimation Stats @EstimationStats]

Updated documentation for the R package can be found at: https://t.co/h6B1b5vSnc

[Estimation Stats @EstimationStats]

Exciting news for dabestr users! We are releasing dabestr version “Lapis” (v2023.9.12), which adds estimation graphics for four new data types: repeated measures, 2 × 2 designs, proportions, and easy meta-analyses. 🧵

[Estimation Stats @EstimationStats]

Contributors to this update were:
Kah Seng LIAN (https://t.co/LqYPdTI1y7),
Zhuoyu WANG (@LucasZhuoyu, https://t.co/nBjy0VvtJ0),
and Jun Yang LIAO (https://t.co/MWJs63oxDX)

[Estimation Stats @EstimationStats]

How do dabestr users prefer to get future releases? (2/2) #rstats #DataScience

[Estimation Stats @EstimationStats]

📢To our R users: We will soon release a new version of dabestr (https://t.co/h6B1b5vSnc) with brand new goodies such as repeated measures, proportion plot, mini-meta and delta-delta. #rstats #datascience We want to know how you prefer to get the package (poll in next tweet): 1/2

[Estimation Stats @EstimationStats]

(Apologies for redoing this tweet thread, our previous images were showing up as sensitive content. This has now been fixed.)

[Estimation Stats @EstimationStats]

Today we are excited to launch DABEST’s first major update. The Python library now offers four new functions: repeated measures, the ∆∆ plot (‘delta-delta’), proportional plot, and mini-meta plot. These expand DABEST’s capabilities to more complex experimental designs. 🧵1/13

[Estimation Stats @EstimationStats]

An early testimonial for v2023.02.14:
https://t.co/GBnYg7931Q

[Ajay Mathuru @MathuruLab]

@EstimationStats @SangyuXu @jacuzzijo @_Jacobluke @ZhangRou99 @myish_irl @adamcchang A very useful update @SangyuXu, @adamcchang and everyone else involved.
We are already using it! 👍🏾