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Tux Machines
Posted by Roy Schestowitz on Dec 30, 2022
> Occasionally I browse the Newest 'jq' questions on Stack Overflow and try to gently expand my jq knowledge, or at least exercise my young jq muscles. This morning I came across this one: Jq extracting the name and the value of objects as an array. Sometimes the questions are hard, sometimes less so. This one didn't seem too difficult, so I thought I'd take a quick coffee break to see what I could come up with (the question had already been answered but I didn't look until later).
> Load Multiple Packages in R, The following example demonstrates how to apply this syntax in practice.
> Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. One of these functions is alias_interaction_plot, which allows the plotting of interactions between a continuous variable and a categorical variable. Importantly, the categorical variable is replaced with an alias variable. This feature allows the back-transformation of the categorical variable to facilitate the communication of the results, for instance, when the categorical variable was sum-coded, which has been recommended for mixed-effects models (Brauer & Curtin, 2018).
> Frequentist and Bayesian statistics are sometimes regarded as fundamentally different philosophies. Indeed, can both methods qualify as philosophies, or is one of them just a pointless ritual? Is frequentist statistics about \(p\) values only? Are frequentist estimates diametrically opposed to Bayesian posterior distributions? Are confidence intervals and credible intervals irreconcilable? Will R crash if lmerTest and brms are simultaneously loaded? If only we could fit frequentist and Bayesian models to the same data and plot the results together, we might get a glimpse into these puzzles.
> This post presents a code-through of a Bayesian workflow in R, which can be reproduced using the materials at https://osf.io/gt5uf. The content is closely based on Bernabeu (2022), which was in turn based on lots of other references. In addition to those, you may wish to consider Nicenboim et al. (2023), a book in preparation that is already available online (https://vasishth.github.io/bayescogsci/book).
> If you’re a Python programmer, you may have heard of the `writelines()` Method. But what exactly is it? The `writelines()` Method is a powerful tool that makes it easy to write a list of strings to a file. You can think of it as a shortcut for writing multiple lines to a file. It’s a great way to save time and effort when writing files.
> The `writelines()` method in Python is a method that is used to write a list of strings to a file. It is a method of the File object in Python, which represents an open file. With `writelines()`, you don’t have to worry about formatting the lines correctly – it does it for you. All you have to do is provide a list of strings and the `writelines()` Method will handle the rest. Another great benefit of `writelines()` is that you can use it with any type of file – from plain text to audio and video files. So if you need a quick and easy way to write to a file, the `writelines()` Method is the perfect solution.
> This new tool, teeplus.py, is an extension of the tee command.
> The tools takes (binary) data from stdin, and sends it to stdout, while also writing the data to a file on disk.
> While the tee command requires a filename as argument, teeplus.py takes no arguments (only options).
> By default, teeplus.py will write the data to a file on disk, with filename equal to the sha256 of the data and extension .vir.
> And it will also log this activity in a log file (teeplus.log by default).
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