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Does anyone know of any good implementation of group-lasso regularized linear regression in R (or even Matlab)?
Have you looked at GNU Octave? It does its work on command line so you can use it with any language that can read/write to file and execute shell commands to kick it off from within the program.
GNU Octave is featured in the Stanford Machine Learning Course on the chapter of linear regression with multiple variables.
There is the grplasso package in CRAN which I believe is what you are looking for.
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I've been looking for a way to conduct group-based trajectory modeling in R with no avail. Something along the lines of what PROC TRAJ (http://www.andrew.cmu.edu/user/bjones/index.htm) accomplishes in SAS. Does anyone know of a similar package in R?
My outcome of interest (the model input) is categorical so i need something that can handle that.
The only package I've been able to find for this in R is crimCV. Here it is on Cran, and here is a working paper by the authors of the package on how it's done. I have not yet investigated this myself (and it seems like it hasn't been updated for years), but this page describes using it to fit a set of trajectories.
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I know with using pack 'real', we can use embedded R in Prolog. But I met problems to use this pack. And I didn't find simple examples of using this pack.
Can somebody give a simple example of this pack?
e.g. Loading Prolog values on to R variables and then call R functions on these values (generate a plot by R function or whatever).
The syntax is the same as the SWISH version I think so just look here:
http://swish.swi-prolog.org/example/Rserve.swinb
and
http://swish.swi-prolog.org/example/Rdataframe.swinb
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I am aware of the http://cran.r-project.org/web/packages/glmnet/index.html and http://cran.r-project.org/web/packages/penalized/index.html packages, but neither of them seems to support Gamma GLMs.
I'd like to utilize elastic net for gamma GLMs in R, what is the easiest way to do it?
(meta: also debating whether this should go on Cross Validated for better responses?)
You can use the HDtweedie package. Gamma is a special case of the Tweedie distribution with p = 2. It's a relatively new package, so expect some teething problems.
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Is there a package for approximating a function using Chebychev Polynomials?
I was formerly using Matlab and there was a package called Compecon that can approximate a function using Chebychev Polynomials.
I am wondering if there is any packages similar for Julia.
Or is there any packages in C or fortran or Python that can do this?
Thanka a lot!
You might want to check out ApproxFun.jl, it may be relevant.
https://github.com/QuantEcon/CompEcon.jl and related https://github.com/QuantEcon/BasisMatrices.jl have the capacity I believe.
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Is there package for R to boost different algorithms? For example Random Forest and neural networks. As I understand, packages ada and gbm can only boost Decision Trees.
Thank you.
take a look at the packages
caret http://cran.r-project.org/web/packages/caret/index.html
C50 http://cran.r-project.org/web/packages/C50/index.html
GAMBoost http://cran.r-project.org/web/packages/GAMBoost/index.html
mboost http://cran.r-project.org/web/packages/mboost/index.html