الدوري الممتاز الهندي 2020

Intro Setup Fundamentals Extensions Endgame Wrap-up Intro The tidyverse “many models” approach was formally introduced in the first edition of R for Data Science (R4DS) in 2017. Since then, the tidyverse has evolved significantly, and along with it, the way we can harness the many models approach. This blog post ... Continue reading: Mastering the Many Models Approach http://dlvr.it/Swp6s5

What if you could 100X your coding productivity? Well you can with ChatGPT. One of the areas I’m most excited about is speeding up the development process of R Shiny web apps. And in this tutorial I’m going to show you how I built an app in 10 minutes... Continue reading: ChatGPT: Made this Shiny App in 10 Minutes http://dlvr.it/Swp6cg

Introduction Boxplots are a great way to visualize the distribution of a dataset. However, sometimes the default ordering of boxplots may not be ideal for the data being presented. In this blog post, we will explore how to reorder boxplots in R ... Continue reading: How to Reorder Boxplots in R: A Comprehensive Guide http://dlvr.it/SwlhD5

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 ... Continue reading: An Educational Stroll With Stan – Part 2 http://dlvr.it/Swlh8z

In the following we show how it is possible to obtain parallelizable, unbiased and computationally cheap estimates of Crossed random effects models with a linear cost in the number of datapoints (and paramaters) exploiting couplings. Crossed random effects models (CREM) CREM model a continuous response variables \(Y\) as depending on ... Continue reading: Linear-cost unbiased estimator for large crossed random effect models via couplings http://dlvr.it/Swj2nm

This post has been cross-posted on the Epiverse-TRACE blog. In a previous post, we discussed a package dependency that goes slightly beyond the normal R package ecosystem dependency: R itself. Today, we step even further and discuss dependencies outside of R: system dependencies. This happens when packages rely on external ... Continue reading: System Dependencies in R Packages & Automatic Testing http://dlvr.it/SwfbqG

Javier Orraca-Deatcu of the Southern California R User Group (SoCal RUG) highlighted his work at a health insurance company for quality of life improvements through data science models. He uses... The post Empowering Healthcare with R: Javier Orraca-Deatcu’s Journey from Finance to Predictive Health Models appeared first on R ... Continue reading: Empowering Healthcare with R: Javier Orraca-Deatcu’s Journey from Finance to Predictive Health Models http://dlvr.it/SwcCGG