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Is there a kind of package in R for this? Is the "AMORE" package a possible surrogate for Matlab's Neural Network Toolbox? Thanks.
the library packagennet offers a lot of functionality for neural networks. Alternatively, there is also neural for MLP and RBF networks. See also www.rseek.org
edit : for multilayer networks, AMORE is the way to go.
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I am currently working on time series project, I have tried SARIMA and Feed Forward neural networks for forecasting.
I found RNN(Recurrent Neural Network) as an interesting approach but am not finding any resources to understand RNN with implementation in R.
Does anyone have some examples of RNN and forecasting in R?
Thanks for the help!
May you should search for ltsm.
In R, you have here some exemples :
https://tensorflow.rstudio.com/blog/time-series-forecasting-with-recurrent-neural-networks.html
And perhaps thiscould be useful, Keras for R :
https://keras.rstudio.com/index.html
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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
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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.