Publications by r on Everyday Is A School Day
Cracking the Code: Unveiling the Hidden Language of USB HID Keyboards!
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? If it can, then how does it work...
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An Educational Stroll With Stan – Part 1
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. Objectives What Is Stan? Load Library & Simulate Simple D...
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Exploring Interaction Effects and S-Learners
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 M...
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Hugging Face 🤗, with a warm embrace, meet R️ ❤️
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 a list of survey com...
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Exploring Causal Discovery with Causal-learn and Reticulate in R
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 pr...
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Exploring Causal Discovery with gCastle through Reticulate in R
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 Causal Inference and Discovery in Python, I’m increasingly struck by the wealth of talent and intelligence out t...
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Unraveling the Effects: Collider Adjustments in Logistic Regression
Simulating a binary dataset, coupled with an understanding of the logit link and the linear formula, is truly fascinating! However, we must exercise caution regarding our adjustments, as they can potentially divert us from the true findings. I advocate for transparency in Directed Acyclic Graphs (DAGs) and emphasize the sequence: causal model -> es...
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From TakeOut to TakeIn: The Savings Simulator
Saving can be enjoyable! If you’re planning to cut down on takeout orders, why not use past data to simulate your savings? Let it inspire and motivate your future dining-in decisions! 👍 If you’re anything like me, you might often resort to takeout due to the convenience of app-based orders and deliveries. These expenses can pile up quickly,...
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What Happens If Our Model Adjustment Includes A Collider?
Beware of what we adjust. As we have demonstrated, adjusting for a collider variable can lead to a false estimate in your analysis. If a collider is included in your model, relying solely on AIC/BIC for model selection may provide misleading results and give you a false sense of achievement. Let’s DAG out The True Causal Model # Loading librari...
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ShinyConf 2023: A Medical Educato(R)’s Journey to Data Science
Special thanks are due to Dr. Amit Diwakar, Dr. Jeffrey Solomon, and Jennifer Hayes for their contributions in making this project a resounding success and continuously striving for improvement. I had the pleasure of presenting our project at ShinyConf 2023 sponsored by Appsilon, “A Medical Educato(R)’s Journey to Data Science: Residency Appli...
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