Journal of Data Science

When collaborating with students, colleagues and practitioners, one soon realizes the lack of efficiency when sending around emails with multiple attachments, especially if changes are made in several types of documents (for example, text, code, PDF) and simultaneously by several collaborators. Using a version control system (VCS) can largely improve joint workflows, from file sharing, including merging changes from different collaborators, to providing access to past versions of the shared work, while allowing each collaborator to work under her/his preferred setup (for example, text editor or file manager). There exists lots of technical or specialized information and literature about VCSes online, but, as often, this is rather overwhelming for beginners. Knowing the basics well is more important than getting lost in the vast amount of possible options VCSes offer. Also, the basics are sufficient to enjoy using VCSes and to see their value in collaborative work, additional features can still be picked up along the way once necessary. We focus on such fundamentals of the centralized VCS SVN and the distributed VCS Git. We explain in simple terms how these systems can be set up and interacted with to increase efficiency in collaborative workflows.

When computations such as statistical simulations need to be carried out on a high performance computing (HPC) cluster, typical questions arise among researchers or practitioners. How do I interact with a HPC cluster? Do I need to type a long host name and also a password on every single login or file transfer? Why does my locally working code not run anymore on the HPC cluster? How can I install the latest versions of software on a HPC cluster to match my local setup? How can I submit a job and monitor its progress? This tutorial provides answers to such questions with experiments on an example HPC cluster.

In the form of a scholarly exchange with ChatGPT, we cover fundamentals of modeling stochastic dependence with copulas. The conversation is aimed at a broad audience and provides a light introduction to the topic of copula modeling, a field of potential relevance in all areas where more than one random variable appears in the modeling process. Topics covered include the definition, Sklar’s theorem, the invariance principle, pseudo-observations, tail dependence and stochastic representations. The conversation also shows to what degree it can be useful (or not) to learn about such concepts by interacting with the current version of a chatbot.

The present paper addresses computational and numerical challenges when working with t copulas and their more complicated extensions, the grouped t and skew t copulas. We demonstrate how the R package nvmix can be used to work with these copulas. In particular, we discuss (quasi-)random sampling and fitting. We highlight the difficulties arising from using more complicated models, such as the lack of availability of a joint density function or the lack of an analytical form of the marginal quantile functions, and give possible solutions along with future research ideas.