Clean, Consistent Column Names
I like to standardize the column names of data I’m reading into R so that I don’t have to match column names from one dataset that has an i.d. column...continue reading.
R News from another blog for R community
TensorFlow feature columns: Transforming your data recipes-style
TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular “recipes” style, creating and subsequently...continue reading.
callr 3.3.0
We have just updated the callr package to version 3.3.0 on CRAN. The biggest change in this release is better support for debugging the background process. See the full changelog...continue reading.
R Weekly 2019-27 R artefacts, reordering, ggchicklet
Hello and welcome to this new issue! Highlight A Short Essay on Duplicated R Artefacts Reordering and facetting for ggplot2! Make Refreshing Segmented Column Charts with {ggchicklet} Insights A Short...continue reading.
Introducing tidylo
Today I am so pleased to introduce a new package for calculating weighted log odds ratios, tidylo. Often in data analysis, we want to measure how the usage or frequency...continue reading.
Chunk Averaging of GLM
Chunk Average (CA) is an interesting concept proposed by Matloff in the chapter 13 of his book “Parallel Computing for Data Science”. The basic idea is to partition the entire...continue reading.
Le Monde puzzle [#1105]
Another token game as Le Monde mathematical puzzle: Archibald and Beatrix play with a pile of n>100 tokens, sequentially picking m tokens from the pile with m being a prime...continue reading.
CRAN Release of R/exams 2.3-3 and 2.3-4
New minor releases of the R/exams package to CRAN, containing a new dedicated function for online quizzes/exams in the Canvas learning management system. Moreover, the update provides a range of...continue reading.
Powerlytics: Female Lifters Who Have Deadlifted over 315 Pounds in Powerlifting Meets
A. Background The growth of womens powerlifting has been a interesting thing to witness over the past decade. There have been massive increases in strength and world records have been...continue reading.
Latin Hypercube Sampling in Hyper-Parameter Optimization
In my previous post https://statcompute.wordpress.com/2019/02/03/sobol-sequence-vs-uniform-random-in-hyper-parameter-optimization/, I’ve shown the difference between the uniform pseudo random and the quasi random number generators in the hyper-parameter optimization of machine learning. Latin Hypercube Sampling...continue reading.
Rhombuses
For a lonely soul, you’re having such a nice time (Nothing in my way, Keane) In my previous post, I created the P2 Penrose tessellation according to the instructions of...continue reading.
10th MilanoR meeting: photos and resources
Curious to know how did our last MilanoR Meeting end up? Check out some photos, resources and the highlights of the night. The post 10th MilanoR meeting: photos and resources...continue reading.
Communication between modules and its whims
As part of the development of a Shiny application for production using {golem}, we recommend, among other things, working with Shiny-modules. The communication of data between the different modules can...continue reading.
dplyr 0.8.3
Introduction We’re pleased (and a little embarrassed) to announce the release of dplyr 0.8.3 on CRAN ? ! This is an emergency release, one week after 0.8.2 to fix a...continue reading.
From empirical to theoretical descriptors of landscapes complexity
The slides are available here.continue reading.
RAND Behavioral Finance Forum, Oct 3, 2019 in Washington DC
SUBMISSION DEADLINE FRIDAY AUGUST 9, 2019 The RAND Behavioral Finance Forum will be holding its annual conference this year on October 3, 2019 in partnership with AARP. The event will...continue reading.
Updates on Microsoft’s R Roadmap in Azure
Yesterday, Microsoft’s AI Customer Engineering Team posted the first in a series of blog posts on the state and future of support for R in Azure. Check out that post...continue reading.
Monotoniebedingungen in Machine Learning Modellen mit R
Monotoniebedingungen können helfen den Sachverhalt besser durch Modelle darstellen zu lassen. In diesem Beitrag wird erklärt wir man solche Monotoniebedingungen in R umsetzt. Der Beitrag Monotoniebedingungen in Machine Learning Modellen...continue reading.
Data Science Bootcamp
Du interessiertst Dich für Data Science und möchtest Deine Daten sinnvoll nutzen? Im Data Science Bootcamp von der STATWORX Data Academy bekommen Einsteiger den perfekten Start in die Praxis. Gemeinsam...continue reading.
SIST thought 1: likelihood and priors for model space
This was a thread on Twitter but I wanted to share it here for a different audience. Today’s thought (actually … Morecontinue reading.
