Rath VS HumesGuillotine

Big fan of D3.js and now there is Observable Plot! I am building several data visualization software for exploratory data analysis:

RATH, auto exploratory data analysis: https://github.com/Kanaries/Rath

GraphicWalker, embeddable data exploration component: https://github.com/Kanaries/graphic-walker

They are using vega-lite for now. But there is a limit of building more fancy and customized visualizations. It seems Plot has a more flexible layer based visualization system that can support larger design space.

Is Plot stable enough now to migrate from vega-lite based system to Plot based? Are there any large milestone or roadmap of Plot in future?

By gaining knowledge of the Business Intelligence once the information is captured from all areas in the business, you can set strategies and define what are the strengths and weaknesses of the business.

I am also working on an Awesome Open Source project named: RATH. Check it out on GitHub!

I'm considering https://github.com/Kanaries/Rath, which seems to be an OSS version of Tableau. Has anyone used it for this type of thing?

Ah, there’s a really nice profiler implemented in one of their other projects here (AGPLv3): https://github.com/Kanaries/Rath/tree/master/packages/rath-c...

There’s a lot of really nice features in this other tool, the author’s thought of everything: https://github.com/Kanaries/Rath

Don't forget to check out my GitHub project:https://github.com/Kanaries/Rath We are also having a website for RATH now!

RATH is an Open Source Automated Data Analysis and Visualization tool that can help you uncover insights and patterns in your data quickly and efficiently. Check out RATH Source Code on GitHub and Free RATH Playground.

As the guy who suggested to Marcus a lossless compression prize to replace the Turing Test, I've got to confess that all this pedantic sophistry "critiquing" algorithmic information is there for a good reason. In the immortal words of Mel Brooks: "We've got to protect our phoney baloney jobs gentlemen!"

https://youtu.be/bpJNmkB36nE

There is actually more at stake here than machine learning. This gets to the root of "bias" in the scientific method. Imagine what horrors, what risks, what chaos would be ours if a truly objective information criterion for causal model selection were to exist! Why, virtually every "sociologist" would be hauled to Hume's Guillotine in a Reign of Terror!

https://github.com/jabowery/HumesGuillotine

But to be clear, Marcus and I have a disagreement about pragmatics of such an approach to dispute processing in the natural sciences. He believes, for example, that the dispute over climate change should be handled by the standard processes in place with academia. My approach differs, based on my hard won experience with reform reforming institutional incentives:

https://jimbowery.blogspot.com/2018/04/necessity-and-incenti...

When it comes to multi-trillion dollar scientific questions, the conflicts of interest become so intense that you really need to apply a gold standard for objectivity and that is the single number: How big is your executable archive of the data in evidence.

While I understand the machine learning world looms as a rival for "unbiased" academic research, it nevertheless remains true that even in this emerging "marketplace of ideas", there is no formal definition of "bias" that disciplines discourse and thereby guides development at the institutional, let alone technical level. Everyone is weighing in with their fuzzy notions of "bias" that betray intense motivations when there has been, for over 50 years, a very clear and present mathematical definition.

See Hume's Guillotine at github for what Musk should be pursuing.

This is one of the reasons I've been advocating a philanthropic prize for macrosocial modeling: Ockham's Guillotine: Beheading the social pseudosciences.