Our group has done a lot of work with non-standard calling conventions in R.

Fred Buchanan, a student at Saint Anselm’s Abbey School, writes:

A feature of creating the VM under a subscription, as opposed to a resource group, is that it creates a new resource group specifically to hold the VM. This simplifies the task of managing and (eventually) deleting the VM considerably. See “Deleting a VM” below.

Automated Lyric Annotation (ALA) Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task. …

Sponsored Post. Machine learning is an incredibly powerful tool for any task involving pattern matching. Some applications, however, such as identifying fraud, detecting network intrusions, mining social network data, and smart search are more efficient when you treat the underlying data as a graph!

In this post I will demonstrate, how my new R package customsteps can be used to create recipe steps, that apply custom transformations to a data set.

Integrating collection, analysis, and interpretation of data in social and behavioral research

What is restez? R packages for interacting with the National Center for Biotechnology Information (NCBI) have, to-date, depended on API query calls via NCBI’s Entrez.For computational analyses that require the automated look-up of reams of biological sequence data, piecemeal querying via bandwith-limited requests is evidently not ideal. These queries are not only slow, but they depend on network connections and the remote server’s consistent behaviour. Additionally, users who make very large requests over extended periods of time run the risk of being blocked.

We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 3rd day of Christmas my true love gave to me…

Samuel Berchuck is a Postdoctoral Associate in Duke University’s Department of Statistical Science and Forge-Duke’s Center for Actionable Health Data Science.