An elegant application of Functional Data Analysis is to model longitudinal data as a curve and then study the curve’s dynamics. For example, in pharmacokinetics and other medical studies analyzing multiple measurements of drug or protein ...

This post is part of a series of technotes about r-universe, a new umbrella project by rOpenSci under which we experiment with various ideas for improving publication and discovery of research software in R. As the project evolves, we will post update...

Date/Time: Wednesday, November 17, 3pm – 5pm GMT / 7am – 9am PST / 10am – 12 noon EST Register now! In this session we will be looking at various... The post Webinar: R Package Management at Roche appeared first on R Consortium.

We’ve released the newest version of NIMBLE on CRAN and on our website. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC). Version 0.12.1, in combination with version 0.12.0 (which was released just last week), ...

This post implements Adams and Deventer (1994) approach which generates the best fitting yield curve smoothing with the principle of maximum smoothness forward rate curve. This provides a powerful closed-form solution by which we can sidestep a kink...

A few months ago, I started hacking together an online e-book on Visualising WRC Rally Stages With rayshader and R. One of the sections (Estimating speeds) described the construction of a simple speed model based around the curvature of the stage route. As part of another sprint into some rally ...

This blog post is a recap of the mlr-org workshop 2021 which took place from the 28th of September until the 10th of October. First of all, we would like to thank all people and organizations which made this workshop possible: our (19) GitHub spon...

It’s been quiet around this blog because supervising two students for Google Summer of Code has kept me pretty busy! But we have some news… Thanks to Mr. Tejasvi Gupta and the support of GSOC, ChemoSpec and ChemoSpec2D were extended to pro...

  Get the code for this blog on GitHub What is this tutorial and who is it for? This tutorial is aimed mainly at R users who want to learn a bit of D3, and specifically those who are interested in how you can incorporate D3 into your existing workflows in ...

Introduction The new gslnls-package provides R bindings to nonlinear least-squares optimization with the GNU Scientific Library (GSL) using the trust region methods implemented by the gsl_multifit_nlinear module. The gsl_multifit_nlinear module was added in GSL version 2.2 (released in August 2016) and the available nonlinear-least squares routines have been ...

Nobody likes a slow dashboard. Heck, I’m closing it and never coming back if it takes more than a couple of seconds to load. It’s a common problem — the datasets are getting bigger and our attention span is getting shorter. Balancing both as a developer, well, sucks. But ...

Make the transition from R User to R Developer with us on November 3rd! With the last Chapter of our “Introduction to R” learning path we conclude our journey towards building strong R programming foundations and skills. With the “Becoming an R deve...

The ggplot2 package is an essential tool in every data scientists toolkit. Today we show you how to use ggplot2 to make a professional heatmap that organizes customers by their sales purchasing habits. R-Tips Weekly This article is part of R-Tips Wee...

Einleitung und Fragestellung In den letzten Jahren1 konnten in diversen Ländern der Aufstieg populistischer Phänomene beobachtet werden: der Aufstieg der AfD in Deutschland, die seit ihrer Gründung binnen 5 Jahren in sämtlichen deutschen Landtagen, d...

This post is inspired by a really approachable post on particle swarm optimisation by Adrian Tam. We’ll build a basic particle swam optimiser in R and try to visualise the results. Libraries # install.packages(pacman) pacman::p_load(dplyr, ggan...

Factorial study designs present a number of analytic challenges, not least of which is how to best understand whether simultaneously applying multiple interventions is beneficial. Last time I presented a possible approach that focuses on estimating the variance of effect size estimates using a Bayesian model. The scenario I used ...