Christian Düben
cdueben.bsky.social
Christian Düben
@cdueben.bsky.social
73 followers 56 following 44 posts
Economist, Data Scientist, Software Developer. Postdoc at Monash University. Melbourne, Australia.
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It is generally a good idea to use TeX Live via a dev container because this enforces the same setup for all coauthors. The container is compatible with but not part of the SoDa Replicator itself because it is not technically required and because it demands prior knowledge of WSL, Docker, etc. 2/2
Since publishing the Monash SoDa Replicator, users have told me about Windows laptops provided by their university not allowing for local TeX Live installation. Hence, I have created a dev container that circumvents this problem: github.com/cdueben/tex_.... #tex #texlive #devcontainer #vscode 1/2
GitHub - cdueben/tex_live_dev_container: Dev Container for TeX Live.
Dev Container for TeX Live. Contribute to cdueben/tex_live_dev_container development by creating an account on GitHub.
github.com
Hence, the package itself is very quick and simple to install from source. It is easy to use and comes with a number of examples.
David Kreitmeir and I published the synthetic matching method from Kreitmeir et al. (2025) as an #rstats package on CRAN (cran.r-project.org/package=synt...). Whereas my other recent R packages are primarily wrappers of my C++ code, synthReturn is a full R package (Fortran only in dependencies).
synthReturn: Synthetic Matching Method for Returns
Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) &lt;<a href="https://doi.org/10.2139%2Fssrn.3751162" target="_top">doi:10.2139/ssrn.3751162</a>&gt;, building...
CRAN.R-project.org
Enjoying my first time at @defcon.bsky.social. Amazing talks, demos, etc. so far. And I met famous journalist @jackrhysider.bsky.social whose inspiring work got me interested in #infosec in the first place. I am gathering a lot of insights for my own research.
#AI is even better than I am in adding seg faults to my code 😅. Maybe vibe coding a Fibonacci heap into my spatial analysis was not the best idea. #cpp
I tried #Positron today. Forking and customizing VS Code instead of further pushing RStudio was a good choice. RStudio is not bad, but Positron seems nicer (and better for #rstats development than VS Code itself). Good job @posit.co👍.
Yes, I agree, Python's package management is a terrible user experience. CRAN feels ok to users, but is a bad package developer experience 😅.
That depends on the field. Many people in my environment (economists) have migrated to Python.
Modern R and Python packages are just C and C++ wrappers anyway. Native R and Python are not performant enough for modern tasks.
Statistical programs in Python tend to build on numpy and pandas. Given how mature and stable these packages are, I would not consider these dependencies a major drawback of Python nowadays.
Base R also only has dense matrices. More elaborate matrix types require a package.
Unfortunately, Julia never gained enough traction and never developed a reasonably sophisticated package ecosystem. So, users will stick to the mediocre choices of #rstats and Python. 6/n
According to my own observation, there is a considerable shift from #rstats to Python among empirical researchers. A key driver of this is the Python-first nature of machine learning APIs. I think that #rstats will lose market share to Python, but it will not die soon. 5/n
For now, the simplicity of #rstats makes it a better choice than Python for many users who do not need the versatility of a general purpose programming language. It keeps attracting users from outdated commercial software like Stata or SPSS. 4/n
The poor management of CRAN does not help either. Both R Core and CRAN need to fundamentally change for #rstats remain popular. 3/n
Compare the release notes of base R and #Python over the course of the past years. Python is leaping forward while base R is devoid of innovation. #rstats is kept alive and thriving by its packages. Unfortunately, the severe limitations of base R also limit the scope of package development. 2/n
There is a lot of childish discussion on the future of #rstats recently on this platform. #rstats is my main language and I will stick to it for a while. However, posts calling it perfect and denouncing any critique as invalid are ridiculous. #rstats has flaws that can threaten its future. 1/n
I need to put together a deep learning model and I do not know whether I should go with #pytorch or #tensorflow + #keras. I have a little experience in the latter and none in the former. What would you recommend for someone who does not care about how pythonic a tool is?
I just had to install the most intrusive #software of my life. It automatically starts at boot, but does not show up in the OS's auto startup list. It does not have an exit button or settings. I have to shut down the background process in the process viewer/ task manager each time. 🫣
😅 Considering R Core's absence of innovation in base R development, a reimplementation in Rust would certainly be a surprise.
Over the course of the past years, I have received a number of emails asking why conleyreg (my first #rstats package) and fixest produce different results. Contributors to an issue in fixest's GitHub repo have now shown that both are economically correct. The assumptions just differ.
We already got complaints from a data editor when code using pre-processed data took a few hours to run on his laptop. Code on raw data does not run at all or takes weeks on end user devices.