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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 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 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 looking for an R implemention of the excellent Glicko-2 algorithm of Mark Glickman. Thusfar I found this one. Although this is a very nice piece of code I am particularly looking for a code that is able to deal with large data frames with match scores (meaning that it is capable of ranking all the players in the data frame in one go). A bit like the way the PlayerRatings package does the trick with e.g. Elo, Glicko. Unfortenately this package doesn't haven an implementation of the Glicko-2 algorithm.
Does anyone have an idea?
Glicko2 and few other algorithms are available in R package sport. Possible for two-player and multi-player matchups. Available on cran and github. Vignette included, standarized syntax, supported by C++.
Quick snippet
# install.packages("sport")
library(sport)
glicko2 <- glicko2_run(formula = rank|id ~ rider, data = gpheats)
# computation results
print(glicko2)
summary(glicko2)
tail(glicko2$r)
tail(glicko2$pairs)
If you had noticed the fine print at the bottom of Mark Glickman's page you would have seen (in tiny text admittedly)
PlayerRatings, an R package implementation of Glicko, as well as a
few other rating systems
with the link being: https://cran.r-project.org/web/packages/PlayerRatings/
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I am looking for an existing package that provides pathfinding algorithms for 2d data. I have a regular grid with scores and would like to start out with the A* algorithm.
I am surprised that there doesn't seem to be an R package dealing with such a task (obviously, googling for "a* algorithm in R" gives very unspecific results).
Does anyone know of an existing package and if there is none, can point me towards an efficient way of implementing the algorithm in R?
Thanks!
There is e1071 and igraph. Not sure if they do A*, but they seem to have other shortest past algorithms.
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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.