With the arrival earlier today of the stochvol package onto the CRAN network for R, our Rcpp project reached a new milestone: 100 packages have either a
LinkingTo:statement on it.
The full list will always be at the bottom of the CRAN page for Rcpp; I also manually edit a list on my Rcpp page. But for the record as of today, here is the current list as produced by a little helper script I keep:
acer apcluster auteur bcp bfa bfp bifactorial blockcluster ccaPP cda classify clusteval ConConPiWiFun EpiContactTrace fastGHQuad fdaMixed forecast fugeR GeneticTools gMWT gof gRbase gRim growcurves GUTS jaatha KernSmoothIRT LaF maxent mets minqa mirt mRMRe multmod mvabund MVB NetworkAnalysis ngspatial oem openair orQA parser pbdBASE pbdDMAT phom phylobase planar psgp quadrupen Rchemcpp Rclusterpp RcppArmadillo RcppBDT rcppbugs RcppClassic RcppClassicExamples RcppCNPy RcppDE RcppEigen RcppExamples RcppGSL RcppOctave RcppRoll RcppSMC RcppXts rforensicbatwing rgam RInside Rmalschains Rmixmod robustgam robustHD rococo RProtoBuf RQuantLib RSNNS RSofia rugarch RVowpalWabbit SBSA sdcMicro sdcTable simFrame spacodiR sparseHessianFD sparseLTSEigen SpatialTools stochvol surveillance survSNP termstrc tmg transmission trustOptim unmarked VIM waffect WideLM wordcloud zic
And not to be forgotten is BioConductor which has another 10:
ddgraph GeneNetworkBuilder GOSemSim GRENITS mosaics mzR pcaMethods Rdisop Risa rTANDEM
As developers of Rcpp, we are both proud and also a little humbled. The packages using Rcpp span everything from bringing new libraries to R, to implementing faster ways of doing things we have before to doing completely new things. It is an exciting time to be using R, and to be connecting R to C++, especially with so many exciting things happening in C++ right now. Follow the Rcpp links for more, and come join us on the Rcpp-devel mailing list to discuss and learn.