Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period” — Part 3: Estimating reproduction numbers

This is the third in a series of posts (previous posts: Part 1, Part 2) in which I look at the following paper: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, vol. 368, pp. 860-868, 22 May 2020 (released online 14 April 2020).  The paper is also available … Read more Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period” — Part 3: Estimating reproduction numbers

Fast.ai in R: How to Make a Computer Vision Model within an R Environment

This talk was presented virtually at eRum 2020 and useR 2020. Learn more about Appsilon‘s ML wildlife preservation project here. Yes, R programmers can make machine learning models, too! In this presentation, we will discuss using the latest techniques in computer vision as an important part of “AI for Good” efforts, namely, enhancing wildlife preservation. … Read more Fast.ai in R: How to Make a Computer Vision Model within an R Environment

Estimating Group Differences in Network Models using Moderation

[This article was first published on Jonas Haslbeck – r, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Researchers are often interested in comparing statistical network models across … Read more Estimating Group Differences in Network Models using Moderation

pointblank v0.4

The pointblank package is now at version 0.4 and is chock full of goodies. There are new functions that allow us to do really interesting things with data validation. Some of the existing functions gained new superpowers. The docs? Better than ever! They’ve been totally revised and there are plenty of easy-to-use examples too. You … Read more pointblank v0.4

Why R? Webinar – Neural Networks for Modelling Molecular Interactions with Tensorflow

[This article was first published on http://r-addict.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. June 25th (8:00pm UTC+2) is a date for next Webinar at Why R? … Read more Why R? Webinar – Neural Networks for Modelling Molecular Interactions with Tensorflow

Color Bars

[This article was first published on Rstats – quantixed, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Here is a fun post about using colour palettes in R. … Read more Color Bars

Consider a permutation test for a small pilot study

[This article was first published on ouR data generation, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Recently I wrote about the challenges of trying to learn too … Read more Consider a permutation test for a small pilot study

Interactively perform a spatial interpolation with the GUInterp Shiny interface

Do you need to perform a spatial interpolation on point data using an interactive graphical interface?You can exploit GUInterp: try it at https://ranghetti.shinyapps.io/guinterp/; install the {guinterp} package with the command remotes::install_github(“ranghetti/guinterp”); launch guinterp::guinterp(); enjoy. A typical spatial interpolation workflow includes common steps: loading point data; filtering them to exclude undesired outlier values; setting the interpolation … Read more Interactively perform a spatial interpolation with the GUInterp Shiny interface

A Bayesian Approach to Linear Mixed Models (LMM) in R/Python

Implementing these can be simpler than you think There seems to be a general misconception that Bayesian methods are harder to implement than Frequentist ones. Sometimes this is true, but more often existing R and Python libraries can help simplify the process. Simpler to implement ≠ throw in some data and see what sticks. (We … Read more A Bayesian Approach to Linear Mixed Models (LMM) in R/Python

Install R without support for long doubles (noLD) on Ubuntu

R packages on CRAN needs to pass a series of technical checks. These checks can also be invoked by any user when running R CMD check on the package tar.gz (to emulate CRAN as much as possible one should also set the –as-cran option when doing so). These checks need to be passed before a … Read more Install R without support for long doubles (noLD) on Ubuntu

How to recreate the matrix of prices from the matrix of returns in R

[This article was first published on Investing Blueprints, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. It is very easy, in R, to obtain a matrix of returns … Read more How to recreate the matrix of prices from the matrix of returns in R

Testing the Effect of Data Imputation on Model Accuracy

[This article was first published on R – Hi! I am Nagdev, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Most of us have come across situations where, … Read more Testing the Effect of Data Imputation on Model Accuracy

Materials from Explanatory Model Analysis Workshop @ eRum 2020

Today I had the pleasure to give a workshop on Explanatory Model Analysis at the eRum 2020 conference. The conference was completely online, so were the workshops. All the materials from my workshop are at http://tiny.cc/eRum2020. The complete three-hour workshop is summarized in this 8-page long cheatsheet. Special thanks to Anna Kozak for the cover. Delivering … Read more Materials from Explanatory Model Analysis Workshop @ eRum 2020

Riddler: Can you solve the not-so-corn maze?

[This article was first published on Posts | Joshua Cook, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. FiveThirtyEight’s Riddler Express link From Tom Hanrahan, a maze you … Read more Riddler: Can you solve the not-so-corn maze?

Represent: geographical breakdown of a virtual seminar series

[This article was first published on Rstats – quantixed, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. During the pandemic, many virtual seminar programmes have popped up. One … Read more Represent: geographical breakdown of a virtual seminar series

Advanced Modelling in R with CARET – a focus on supervised machine learning

Yesterday (17th June) I did a session with the NHS-R Community relating to how to use the brilliant CARET package to build, evaluate, train and improve machine learning models. This was an entry level webinar to highlight the possibilities of how supervised machine learning could be used in the NHS to inform and augment decisions. … Read more Advanced Modelling in R with CARET – a focus on supervised machine learning

Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period” — Part 2: Proxies for incidence of coronaviruses

This is the second in a series of posts (previous post: Part 1) in which I look at the following paper: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, vol. 368, pp. 860-868, 22 May 2020 (released online 14 April 2020).  The paper is also available here, with … Read more Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period” — Part 2: Proxies for incidence of coronaviruses

2 Months in 2 Minutes – rOpenSci News, June 2020

rOpenSci HQ The rOpenSci team, together with ten expert community members, put together a post: When Field or Lab Work is not an Option – Leveraging Open Data Resources for Remote Research. We highlighted examples of how specific collections of packages are being used right now in fields as varied as archaeology and climate science … Read more 2 Months in 2 Minutes – rOpenSci News, June 2020

From R Hub – Counting and Visualizing CRAN Downloads with packageRank (with Caveats!)

[This article was first published on R Consortium, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Originally posted on the R Hub blog This post was contributed by Peter … Read more From R Hub – Counting and Visualizing CRAN Downloads with packageRank (with Caveats!)

Penguins Dataset Overview – iris alternative in R

If there’s a dataset that’s been most used by data scientists / data analysts while they’re learning something or coaching something – it’s either iris (more R users) or titanic (more Python users). iris dataset isn’t most used just because it’s easy accessible but it’s something that you can use to demonstrate many data science … Read more Penguins Dataset Overview – iris alternative in R

Fooled By Randomness

I recently read the great book Fooled By Randomness by Nicholas Nassim Taleb. There is a nice illustrative example in there on the scaling property of distributions across different time scales. The example is formulated as the hypothetical example of a dentist who has set up a home trading environment and started to invest in … Read more Fooled By Randomness

Time Series in 5-Minutes, Part 2: Autocorrelation and Cross Correlation

[This article was first published on business-science.io, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Have 5-minutes? Then let’s learn time series. In this short articles series, I … Read more Time Series in 5-Minutes, Part 2: Autocorrelation and Cross Correlation

In r-spatial, the Earth is no longer flat

[This article was first published on r-spatial, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. [view rawRmd] Summary: Package sf is undergoing a major change: all operationson geographical … Read more In r-spatial, the Earth is no longer flat

Hello hordes!

[This article was first published on Colin Fay, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Introducing hordes, a module that makes R available from NodeJS. About General … Read more Hello hordes!

Impute missing data for #TidyTuesday voyages of captive Africans with tidymodels

[This article was first published on rstats | Julia Silge, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. This week’s#TidyTuesday datasets reflect on Juneteenth, the date when the … Read more Impute missing data for #TidyTuesday voyages of captive Africans with tidymodels

sans sérif & sans chevron

[This article was first published on R – Xi’an’s Og, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. {\sf df=function(x)2*pi*x-4*(x>1)*acos(1/(x+(1-x)*(x<1)))} As I was LaTeXing a remote exam for … Read more sans sérif & sans chevron

Easy steps to develop and publish your first R package

Be careful while writing the text under Description. The text under this title can be written in multiple lines and the text per line must be within some word limits. Also, the next line must start with four spaces. Since R packages are open-source tools, it is important to provide a suitable license for your … Read more Easy steps to develop and publish your first R package

eRum 2020: Appsilon Presentations On xspliner, fast.ai, and Writing Production-Ready R Code

As you may already know, the 2020 European R Users Meeting will be a virtual event. This year, Appsilon engineers Krystian Igras, Marcin Dubel, and Jędrzej Świeżewski will be giving virtual presentations on Friday, June 19th. Tune in to learn about xspliner, making production-ready R code, and using R for Machine Learning projects with fast.ai. … Read more eRum 2020: Appsilon Presentations On xspliner, fast.ai, and Writing Production-Ready R Code

Market Making and Win/Loss

The article https://online.wsj.com/public/resources/documents/VirtuOverview.pdf is a neat little illustration of a simple asymptotic toy distribution given an initial probability of a win or loss per-trade. It is used as an example to illustrate the basic methodology behind the working market-maker business – develop a small edge and scale this up as cheaply as possible to maximise … Read more Market Making and Win/Loss

David Robinson’s R Programming Screencasts

David Robinson (aka drob) is one of the best known R programmers. Since a couple of years David has been sharing his knowledge through streaming screencasts of him programming. It’s basically part of R’s #tidytuesday movement. Alex Cookson decided to do us all a favor and annotate all these screencasts into a nice overview. https://docs.google.com/spreadsheets/d/1pjj_G9ncJZPGTYPkR1BYwzA6bhJoeTfY2fJeGKSbOKM/edit#gid=444382177 … Read more David Robinson’s R Programming Screencasts

ggplot2 Text Customization with ggtext | Data Visualization in R

ggplot2 is go-to R package for anyone who wants to make beautiful static visualizations in R. But most ggplot2 gplots look almost the same and little many data analysts or data scientists care about customizing it, primarily because it’s not very intuitive to do so. That’s where ggplot2 extensions come in very handy. ggtext is … Read more ggplot2 Text Customization with ggtext | Data Visualization in R