I found Polars syntax is quite similar to dplyr. And the way that we can chain the functions makes it even more familiar! It was fun learning the nuances, now it’s time to put them into practice! Wish me luck! 🍀 Motivation In preparation for using more Python in 2025 and ...

‘Fascinating’ describes my journey with Stable Diffusion 3. It’s deepened my appreciation for original art and masterpieces. Understanding how to generate quality art is just the beginning—it drives me to explore the underlying struc...

Overall, I am quite impressed with the responses! With minimal prompt engineering, document cleaning! It was able to return accurate responses, and even separated different conditions and provided appropriate treatment options. It was also able to return the correct response for tricky questions that our RAG was not able to. ...

MCAR, MAR, MNAR, all so confusing. But with DAG, oh so amusing! Many technical words, I don’t understand, but with simulation, I am a fan! Join me in exploring missing mechanisms, learn I will with great optimism. Visualizing M...

The SUTVA, Positivity, Identifiability, Consistency, Exchangeability of Causal Inference, the essential ingredients that helps us bring out the true flavor of the causal model. Here is my understanding of each assumptions (main course) with examples (side dish) and accompanied by simulation (paired with beverages). Bon Appétit! Since the multiple ...

I’ve struggled with differentiating between total, direct, and indirect effects, so this blog/note serves as a personal reference to solidify my understanding and make future amendments as needed. While there are comprehensive articles available...

It was enjoyable to visualize the non-linear relationship with interaction and observe the corresponding changes in CATE. If one understands the underlying equation, it’s possible to easily obtain the ATE using calculus. Lastly, adopting Richard...

I’m now more confident in my understanding of the 95% confidence interval, but less certain about confidence intervals in general, knowing that we can’t be sure if our current interval includes the true population parameter. On a brighter note, if we have the correct confidence interval, it could still ...

We learned how to convert the pooled odds ratio from a random-effects model and subsequently calculate the number needed to treat (NNT) or harm (NNH). It’s important to understand that without knowing the event proportions in either the treatment or c...

Here, we have demonstrated three different methods for calculating NNT with meta-analysis data. I learned a lot from this experience, and I hope you find it enjoyable and informative as well. Thank you, @wwrighID, for initiating the discussion and pro...

What an incredible journey it has been! I’m thoroughly enjoying working with Stan codes, even though I don’t yet grasp all the intricacies. We’ve already tackled simple linear and logistic regressions and delved into the application ...

Diving into this, we’re exploring how using numbers to express our certainty, especially with medical results, can help sharpen our estimated ‘posterior value’ and offer a solid base for learning and discussions. We often talk about ...

I learned a great deal throughout this journey. In the second part, I gained knowledge about implementing logistic regression in Stan. I also learned the significance of data type declarations for obtaining accurate estimates, how to use posterior to ...

There is a lot to learn about Bayesian statistics, but it’s fun, exciting, and flexible! I thoroughly enjoyed the beginning of this journey. There will be learning curves, but there are so many great people and resources out there to help us get closer to understanding the Bayesian way. ...

Sending key presses to another device using software that emulates a keyboard, but isn’t a physical keyboard, is a fascinating concept. We understand that in the Linux/Unix environment and with Python, this can be accomplished through low-level programming. But can the R programming language achieve the same feat? ...

Interaction adventures through simulations and gradient boosting trees using the S-learner approach. I hadn’t realized that lightGBM and XGBoost could reveal interaction terms without explicit specification. Quite intriguing! picture resembles interaction 🤣 Objectives: What is interaction? Simulate interaction Visualize interaction True Model ✅ Wrong Model ❌ What is S Learner? What is ...

I’m delighted that R users can have access to the incredible Hugging Face pre-trained models. In this demonstration, we provide a straightforward example of how to utilize them for sentiment analysis using GPT-generated synthetic data from evaluation comments. Let’s go! Interesting Problem 😎 What if you’re faced with ...

The PyWhy Causal-learn Discord community is fantastic! The package documentation is equally impressive, making experiential learning both fun and informative. Truly, it’s another exceptional tool for causal discovery at our fingertips! ❤️ It’s time to delve into PyWhy’s Causal-learn! his brief blog post leverages the framework from a ...

Get ready for a thrill ride in causal discovery! We’re diving into gCastle, a Python package, right in R to amp up our skills. Let’s orchestrate our prior knowledge and nail that true DAG. 🔥 As I delve into Aleksander Molak’s Causa...