R-bloggers

R-bloggers is a platform that leverages the content from various documents to provide concise summaries for its audience. The platform serves as a hub for information and insights related to a wide range of topics. By summarizing the content from diverse sources, R-bloggers aims to offer valuable and easily digestible information to its readers. The platform’s summaries are designed to provide a quick overview of the key points and insights covered in the original documents, making it a convenient resource for individuals interested in staying informed about the latest developments in various fields.

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Model-agnostic prediction intervals in Python and R: does nnetsauce’s `QuantileRegressor` hold up?

R-bloggers

Point predictions tell you what a model *thinks* will happen. They don't tell you how much to trust that number. nnetsauce's `QuantileRegressor` takes any sklearn-compatible regressor and turns it int...

chilemapas is back on CRAN

R-bloggers

Maps of the Political and Administrative Divisions of Chile Continue reading: chilemapas is back on CRAN

How to Create a Function in R: Custom Median & ggplot2 Examples

R-bloggers

If you've written the same block of R code three times this week — a group summary, a plot, a cleanup step — you don't need another package. You need a function. A custom function is the solution. For...

Navigating Challenges in Spatial Machine Learning

R-bloggers

Spatial machine learning has become a standard tool for producing environmental and geographic prediction maps. It is now relatively (technically) easy to combine field observations with remote sensin...

ahead (Time Series Forecasting with uncertainty quantification) gets a lot faster to install: most dependencies are now optional

R-bloggers

ahead (R) 0.38.1 and its Python wrapper now install in a fraction of the time, by moving almost every heavy modeling dependency from Imports to Suggests and installing them at runtime, only when a fun...

Research Workflows

R-bloggers

I’m teaching MPTC again this semester and have been developing a few workflow slides. Here’s a popular one. The Scientific Process. Now if you’ll excuse me there’s a mandatory webinar I need to click ...

Visualising High-dimensional Data with R workshop

R-bloggers

Join our workshop on Visualising High-dimensional Data with R, which is a part of our workshops for Ukraine series! Here’s some more info: Title: Visualising High-dimensional Data with R Date: Thursda...

Bringing BERT — R functions in Excel — up to R 4.6

R-bloggers

BERT lets you call R functions from Excel cells, but its author stopped work in 2018. Bringing it up to R 4.6 — and to working on every R from 3.5 up — turned up a deadlock that froze Excel and two bu...

R For SEO Part 10: SEO Reporting With Google Sheets & OpenRouter

R-bloggers

R For SEO Part 10: SEO Reporting With Google Sheets & OpenRouter Welcome back, and we’re finally at the end of my R for SEO series, at least for now. We’ve gone through quite... This post was written ...

Slicing in tidyomics

R-bloggers

Introduction The tidyverse verb slice() lets you select observations by position, returning a subset of the data based on integer indices. There are also some related convenience functions, which retu...

Z-Test in R: Complete Guide with One-Sample & Two-Sample Examples

R-bloggers

Complete guide to running one-sample and two-sample Z-tests in R using BSDA::z.test(), the manual pnorm/qnorm method, and how to choose between a Z-test and a t-test for your data. Read More Continue ...

ROC, Paper, Scissor, Shoe

R-bloggers

📊 Working through ROC-AUC from scratch, then poking at its blind spots — low prevalence, calibration, and finally Decision Curve Analysis. Mostly notes to myself on what I learned (and got confused b...