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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'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 want to generate random points of uniform density over the unit ball [-1,1]^d in R.
Are there any R packages which offer this functionality?
I am sure i can do this myself by extending this answer: https://math.stackexchange.com/a/87238/250498 to d dimensions.
But i want to know if there is any function or package in R that already does this.
It would be useful if there is a package which can generate standard multivariate distributions instead of me having to sample them myself using rejection sampling or other techniques.
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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 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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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.