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I'm looking for an integration of ImageJ and R. There was a package called RImageJ as referenced in [1][2] for previous versions of R (I'm using R 3.1.2 (2014-10-31) -- "Pumpkin Helmet" ) but it isn't available anymore.
Basically what I'd like to try is a region growing algorithm over an image in R. Does anybody know about an updated version of RImageJ or another package allowing this algorithm?.
Thanks ;)
[1] http://romainfrancois.blog.free.fr/index.php?category/R-package/RImageJ
[2] http://cmci.embl.de/documents/101105ij_r_jython#using_imagej_from_r
The Bio7 project is a version of ImageJ which features integration with R, among other improvements.
It is also worth mentioning that the ImageJ2 project has a project under development called scripting-r, which seeks to provide a JSR-223-compliant scripting language built on top of RServe. However, it is not yet functional. Once it is, it will be possible to execute R code as a script from within ImageJ's Script Editor.
Edit 2016-09-26: As of this writing, the SciJava scripting-r project is now built on Renjin, an implementation of R on top of the JVM. Huzzah!
I've used the pixmap package to do image processing in the past, but it's pretty bare-bones. It might also be worth looking into the package geomorph.
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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'm studying about sparkR, and I know there are so many useful R package in CRAN. But it seems like R package can't be used in sparkR. I'm not sure about that.
Is it true??? If it's not, could you explain how import R package into sparkR?
I'm guessing that you may be referring to the includePackage command:
SparkR also allows easy use of existing R packages inside closures.
The includePackage command can be used to indicate packages that
should be loaded before every closure is executed on the cluster.
Source: R frontend for Spark
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Despite that the Writing R Extensions manual mentions f90/f95. It seems that it is not well supported, especially it is mixed with C++.
https://stat.ethz.ch/pipermail/r-devel/2015-March/070851.html
But if I have to use f90/f95 code with Rcpp. Is there a walkaround currently available? Thanks.
(I think I just answer I rather similar question on rcpp-devel.)
Citing from Writing R Extensions, Section 1.1.5 with my use of bold face below:
The sources and headers for the compiled code are in src, plus
optionally a file Makevars or Makefile. When a package is installed
using R CMD INSTALL, make is used to control compilation and linking
into a shared object for loading into R. There are default make
variables and rules for this (determined when R is configured and
recorded in R_HOME/etcR_ARCH/Makeconf), providing support for C, C++,
FORTRAN 77, Fortran 9x15, Objective C and Objective C++16 with
associated extensions .c, .cc or .cpp, .f, .f90 or .f95, .m, and .mm,
respectively. We recommend using .h for headers, also for C++17 or
Fortran 9x include files. (Use of extension .C for C++ is no longer
supported.) Files in the src directory should not be hidden (start
with a dot), and hidden files will under some versions of R be
ignored.
It is not portable (and may not be possible at all) to mix all these
languages in a single package, and we do not support using both C++
and Fortran 9x. Because R itself uses it, we know that C and FORTRAN
77 can be used together and mixing C and C++ seems to be widely
successful.
You would have to ask R Core why they chose to outlaw this. Usually, this means some technical reason. I do not use Fortran so I can't help with th reasoning behind it.
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I just searched on google and r-project.org but couldn't find anything.
Is there a package in R for statistical relational learning?
Link to wikipedia:
http://en.wikipedia.org/wiki/Statistical_relational_learning
Yes, there are quite a few actually.
On CRAN, look at the gRaphical Models Task View.
Under each header is a listing of R Packages subsumed under that subject header--the first being Representation, manipulation and display of graphs.
Scroll down this page to the last three section headers:
Miscellaneous: Model search, specialized types of models etc.,
Bayesian Networks/Probabilistic expert systems; and
BUGS models, just below it.
Under these three headers are a total of 16 R Packages (seven, three and six packages, respectively). Within these nine, you should have no trouble finding a couple most suited for your project.
The only one I have personally used is bnlearn, a Package for Bayesian network structure learning. This is not my field, so I recall that the Package documentation (Manual and Vignette) are excellent and include working code examples.
Finally, for visualizing these models, I recommend highly the R bindings to the excellent Graphviz Library; the R bindings are gathered in the R Package, RGraphviz. RGraphviz is not available on CRAN but rather on Bioconductor; it depends on GraphViz.
Not yet, actually. Doug's answer is inherently misleading. If someone's looking for non-R implementation of the requested models, I may provide this link.
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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/