الدوري الممتاز الهندي 2020

The latest version of the opencv R package can detect and decode QR codes! # Install latest opencv install.packages("opencv", repos = " https://ropensci.r-universe.dev") /> There are two ways of using this: the ocv_qr_detect function tries... Continue reading: Scanning QR codes in R http://dlvr.it/SyC18c

Introduction Randomness is an essential part of many statistical and machine learning tasks. In R, there are a number of functions that can be used to generate random numbers, but the runif() function is the most commonly used. The runif() func... Continue reading: Randomness in R: runif(), punif(), dunif(), and quinf() http://dlvr.it/Sy8s8D

In a previous post, I looked at how Google Scholar ranks co-authors. While I had the data available I wondered whether paper authorship could be used in other ways. A few months back, John Cook posted about using Jaccard index and jazz albums. The idea is to look at the ... Continue reading: All The Right Friends II: clustering papers using Google Scholar data http://dlvr.it/Sy7Jmq

Zhenguo Zhang's Blog /2023/10/23/r-tidy-evaluation-in-ggplot2/ - Since ggplot2 version 3.0.0, it started to support tidy evaluation, and use the technique to replace aes_ and aes_string(). In this post, I would like to show how the syntax of ggplot2 ... Continue reading: [R] tidy evaluation in ggplot2 http://dlvr.it/Sy6WjF

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... Continue reading: Approaches to Calculating Number Needed to Treat (NNT) with Meta-Analysis http://dlvr.it/Sy5WQ4

Recently, during work on some forecasting task at PowereX, I stumbled upon an interesting improvement in time series forecasting modeling. In general, in regression modeling, we use evaluation/loss metrics based on one step ahead (one value) horizon. B... Continue reading: Multistep horizon loss optimized forecasting with ADAM http://dlvr.it/Sy3n5X

It’s not common for Shiny Developers and Data Scientists to have strong design and frontend/web development skills. They usually prefer working with data, quickly visualizing it, and try to avoid diving into the complexities of CSS.  Getting buy-in within an organization for technological projects is hard. Many decision-makers ... Continue reading: Innovate Faster with {ShinyUiEditor}: Effortless Shiny App Prototyping http://dlvr.it/Sy1NhZ