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Can someone help me with some tutorials on how I can learn to formulate optimization problems in R?
I am mainly looking for 2d optimization problems. Until now I have found a lot of info on optimization algorithms, however almost nothing on how to implement them with 1 or 2 examples.
Well, the R task guides are always a good place to start.
In addition, there was a recent article on the optimx package in R
Also, if you load up some packages listed in the task guide, then you will probably find some useful vignettes (I've learned an awful lot of stats from those vignettes over the years).
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So if I am a newbie programmer in Julia and not sure how to really get started or keep getting stuck with language-specific things, what are my resources to help me answer my Julia related programming questions.
There's so many! See the list below:
Julia docs: https://docs.julialang.org/en/v1/
Julia Slack: https://slackinvite.julialang.org
Julia Discourse: https://discourse.julialang.org
JuliaAcademy: https://juliaacademy.com
Posting question on StackOverflow with the julia tag.
In general, the community is here to help! Please feel free to use some or all of these resources.
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I am building a path for beginners (like me) to guide them through the learning of data analysis in R (Only in R please).
Would you suggest me any new sections and/or new courses that i should add?
Heres is what i have been adding till now: http://studiy.co/path/data-analysis/
Thanks for the help!
well you need to distinguish... If you want just to learn R programming you may check Coursera course on R
https://www.coursera.org/learn/r-programming
that would give you some basics. If you need to learn how to analyse data then you may need a bit more. What are your background in mathematics? What kind of statistics know-how do you have? A GREAT resource in my opinion is the book
An Introduction to Statistical Learning with applications in R
That you can even find for free in pdf (http://www-bcf.usc.edu/~gareth/ISL/). Beware that you may need some mathematics background to be able to understand it fully. In case you have a more profound background in science I may have other suggestions. Could you tell us at what level are you?
Hope that helps.
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I'm trying to learn to write elegant Standard ML code by reading others' code or projects. Does anyone know of some good code/projects?
I found the MLton compiler to be a great source for learning the module system.
The Twelf theorem prover also has a lot of high quality examples.
The standardml github account has a number of projects of varying quality. Somehow my sml-ext library ended up there. I'm not sure how.
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I'm trying to get started using some statistical analysis with the limma package that runs out of R. Any one know a good tutorial?
The limma web-page has plenty of material. I've also written a tutorial paper that analyses yeast microarray data using (amongst other things) limma.
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Other than the excellent SO answer here, and the Roxygen manual and vignette, is there any particularly thorough guide to using Roxygen?
I'm working on a guide (but it's still incomplete).
A more comprehensive set of vignettes have been added to the package, and were made available on CRAN as of version 4.0.0.
The user2010 tutorial that was presented by the roxygen authors is available here. It's not really a thorough guide, but still very useful I think.
Even with the refrences above, I still had trouble finding a good kickstarter example until I found this other
Github page.