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Is there any function/package to perform k-Nearest Neighbor based density estimation in R?
class package: function knn
ipred package: function ipredknn
This search on Baron's Site brings up 29 hits.
And the Multivariate Task View has two sections on classification, and has links to a few packages that do knn and related tasks.
I haven't used it, but the the kknn package might help.
You might also look at class and knncat for classification using k-nearest neighbor functions.
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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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This is a general question. Are there classifiers in R -- functions that perform classification implementing classification algorithms-- that accept as input argument the relative cost of misclassification. E.g. if a misclassification of a positive to negative has cost 1 the opposite has cost 3.
If yes which are these functions?
Yes. If you are using the caret package (you should; it provides 'standardization' for 200+ classification and regression methods by wrapping almost all relevant R's packages), you can set the weights argument of the train function (see p.152; see also here) for models that support class weights. This answer lists some of the models that support class weights.
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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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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