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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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Please can somebody help me with this, BENOIT BELIAEFF and THIERRY BURGEOT (2002) created the INTEGRATED BIOMARKER RESPONSE (IBR), however I did not find any tool for R that would help in this calculation, does anyone know any?
Did you know that actually that index was revisited by Devin et al. 2014 (DOI 10.1007/s11356-013-2169-9)? Try looking into it when you can!
But, about the R package: I was also having troubles finding one that actually does this type of calculation, and what I found was this new package called IBRtools
https://github.com/ecologicaltools/IBRtools
It contains a function called ibr_index that gives you a list of the index values and also the mean of the index values.
Furthermore, it also provides a function to graph your results, called ibr_chart, that creates a radarchart based on standardized values created by function ibr_std
I would definitely recommend looking into the package help section for further clarifications!
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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'm trying use linear regression with elastic net penalties for a mixed model. In the past I have used the R glmnet package with the penalty.factors parameter to accomplish this, but R is no longer an option for my project. I have found several implementations of elastic net in different languages, but none which allow for different penalty factors for each feature the way that the R glmnet package does.
Does a true translation of the R glmnet function exist in any other language? Thanks!
EDIT: Note that the R code is actually a wrapper of the FORTRAN code written for the R package. A few other wrappers exist in other languages, but I am looking for an new implementation of the full algorithm, not just an alternative wrapper. I want to be able to edit the code a bit, but I am not at all familiar with FORTRAN.
I would have put this in comments if I had enough points, but there is an implementation in Matlab, written by a few members of the team that wrote the original glmnet package:
http://web.stanford.edu/~hastie/glmnet_matlab/
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I'm interesting in evolutionary algorithms. I have tested genetic algorithms with R but has someone tried with genetic programming? Do you know, if there are code somewhere written in R.
I'm not aware of anything on CRAN for genetic programming in particular, although there is some work being done on the problem. In particular, have a look at the RGP package that is still in development.
Otherwise, have a look at the RFreak package, which is a wrapper for the "Free Evolutionary Algorithm Kit".
You might want to have a look at genalg, at GA and at galgo
There is a new package available:gramEvol: Grammatical Evolution for R
A native R implementation of grammatical evolution (GE). GE
facilitates the discovery of programs that can achieve a desired goal.
This is done by performing an evolutionary optimisation over a
population of R expressions generated via a user-defined context-free
grammar (CFG) and cost function.
You can find the vignette here:Grammatical Evolution: A Tutorial using gramEvol
UPDATE: I posted an article on my blog about it:
Symbolic Regression, Genetic Programming… or if Kepler had R.
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Are there any R-project packages that facilitate asynchronous network IO?
I'm think here along the lines of Ruby's Eventmachine or Python's Twisted.
If there are several such packages/libraries which is the best in terms of:
- performance
- features
First of all, R is single-threaded, so typically people try to use parallel computing approaches (see, for instance, the snow package). I don't think there's anything quite like Eventmachine or Twisted.
Check out the following:
The "State of the Art in Parallel Computing with R" paper describes most of the approaches to parallel computing in R (http://www.jstatsoft.org/v31/i01/paper). There are many useful packages in the HighPerformanceComputing view: http://cran.r-project.org/web/views/HighPerformanceComputing.html.
Check out svSocket: http://cran.r-project.org/web/packages/svSocket/
You can try using NetWorkSpaces with R: http://cran.r-project.org/web/packages/nws/.
There are several examples of R servers. RServe: http://www.rforge.net/Rserve/
The iBrokers packages is one of the only ones that I know which uses asynchonous requests. Have a look at the source code for that package (you can download it off R-Forge) and the related vignette: http://cran.r-project.org/web/packages/IBrokers/vignettes/RealTime.pdf
The biocep project also includes many relevant features: http://biocep-distrib.r-forge.r-project.org/