David's reply is far more comprehensive, but it may be worth adding that new "data mining" packages are being added almost daily to R software repositories (CRAN, github, etc.), so that anything one would say about this becomes almost instantly outdated. e.g. from a post 4 days ago here from Nan Xiao:
----- "- I am pleased to announce that the R package OHPL is now available on CRAN (https://CRAN.R-project.org/package=OHPL). The package implements the ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <doi:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data. For more information, please see https://OHPL.io."; ---- You certainly wouldn't find this in SAS software! Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Aug 15, 2017 at 6:34 PM, David Winsemius <dwinsem...@comcast.net> wrote: > >> On Aug 14, 2017, at 12:22 PM, fs <m...@friedrich-schuster.de> wrote: >> >> Hi, and sorry for asking such an unspecific question. >> >> Does anybody know of statistical / data mining methods that are available in >> R >> that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS >> Enterprise Miner. I don't expect a complete list, just two or three examples >> or hints where and what to look for. >> >> I found some older comparisons, and the R methods mentioned there (GLMET, RF, >> ADABoost) are now supported by SAS (at least to some degree). >> >> And there exists a (massive) list of available models for the caret package >> here: https://rdrr.io/cran/caret/man/models.html, but it's hard to analyze >> the >> complete list. >> >> (I'm trying to answer a question of a colleague). > > It wasn't clear whether it was statistical procedures themselves or > connections to back-end data and machine learning packages might be the > metric of comparison. I also thought the question would have been better > posted on a SAS website, since the CRAN Task Views provide an even more > complete listing and most of us are not current users of the SAS Enterprise > Miner Suite. The SAS users might have a better notion of their capacities and > limitations. > > You might start by comparing: > > 1) > https://www.sas.com/content/dam/SAS/en_us/doc/factsheet/sas-enterprise-miner-101369.pdf > > ... although that did not appear to be a comprehensive listing of available > model types. > > With: > > 2a) https://cran.r-project.org/web/views/MachineLearning.html > 2b) https://cran.r-project.org/web/views/Bayesian.html > 2c) https://cran.r-project.org/web/views/ExtremeValue.html > 2d) https://cran.r-project.org/web/views/FunctionalData.html > 2e) https://cran.r-project.org/web/views/Robust.html > 2f) https://cran.r-project.org/web/views/SpatioTemporal.html > 2g) https://cran.r-project.org/web/views/Spatial.html > > Left out several Task Views since they might be probably too "ordinary", but > you should look at all of them: > https://cran.r-project.org/web/views/ > > > Other websites possibly outlining areas of possible difference: > > https://tensorflow.rstudio.com/ > > https://blog.rstudio.com/2016/09/27/sparklyr-r-interface-for-apache-spark/ > > https://spark.rstudio.com/reference/sparklyr/latest/ml_multilayer_perceptron.html > > https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/TensorFlow-MNIST/td-p/318708 > > https://thomaswdinsmore.com/2017/04/05/sas-peddles-open-source-fud/ > > > > -- > David Winsemius > Alameda, CA, USA > > 'Any technology distinguishable from magic is insufficiently advanced.' > -Gehm's Corollary to Clarke's Third Law > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
