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Formidable argument.


This line of thought seems to rely on existence and verified existence being co-extensional (if not equivalent). Perhaps Kurt Gödel showed us otherwise.


It's an excellent list. A little amusing as Dennett's own debating style is a touch more robust than this might suggest. Though in all fairness, if it's him vs. another philosopher of similar standing, both with subtle and complex positions built up over 40+ year careers, working through steps 1 - 3 might lengthen the discussion somewhat.


To expand on the free point - it's not just that SAS and SPSS have a price tag. You're then supposed to pay for a load of bolt-on modules. The extreme case is if you want to do decision trees in SAS, they've made a commercial decision that you must be doing some kind of 'Enterprise-Grade Data Mining', and therefore need to pay their 5-figure price tag rather than their 4-figure price tag.

I think this has been a big barrier to any sort of package ecosystem to rival CRAN. Even if your entire target audience has SAS or SPSS, if your new package depended on one or more of those modules, it's going to cost some of your prospective users hard cash to run it. Whereas R just installs the dependencies for you.


Agree entirely. Academics led and industry followed.


SAS a bit, but as another comment notes, nothing like CRAN. SPSS not at all, at best you could publish some sort of script.


Yup, my first thought too. Though presumably different roots - player and pliers. There's also another stringr (crowdsourced video content platform) and caret (text editing for Chrome).


Now dplyr has pretty much replaced it with better syntax and perf. Take a look at it. https://github.com/hadley/dplyr


One thing I've been curious about with R:

Why is it that popular libraries in R are replaced with other names when enhanced? I.e., plyr->dplyr, reshape->reshape2, etc.? Is it a namespacing thing? I guess if there's not a universally accepted way of versioning library imports that would explain it as well.


"dplyr is the next iteration of plyr, focussed on tools for working with data frames (hence the d in the name)."


plyr does more than dplyr (I perfer dplyr)

reshape does more than reshape2 which does more than tidyr

They actually get more refined and are different creatures with similar features and not different versions.


That could make for an interesting filter bubble


I've found Evernote's web clipper to be a good replacement for that same process.


This starts with something on measure theory: http://www.metacademy.org/graphs/concepts/bayesian_parameter...


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