CRAN Package Check Results for Package classmap

Last updated on 2021-10-24 06:51:44 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.1 12.48 1087.45 1099.93 OK
r-devel-linux-x86_64-debian-gcc 1.1.1 11.09 839.27 850.36 OK
r-devel-linux-x86_64-fedora-clang 1.1.1 1308.43 NOTE
r-devel-linux-x86_64-fedora-gcc 1.1.1 1318.72 NOTE
r-devel-windows-x86_64 1.1.1 20.00 0.00 20.00 FAIL
r-devel-windows-x86_64-gcc10-UCRT 1.1.1 NOTE
r-patched-linux-x86_64 1.1.1 13.04 117.06 130.10 OK
r-patched-solaris-x86 1.1.1 238.10 NOTE
r-release-linux-x86_64 1.1.1 8.37 116.29 124.66 OK
r-release-macos-arm64 1.1.1 NOTE
r-release-macos-x86_64 1.1.1 NOTE
r-release-windows-ix86+x86_64 1.1.1 22.00 152.00 174.00 OK
r-oldrel-macos-x86_64 1.1.1 NOTE
r-oldrel-windows-ix86+x86_64 1.1.1 28.00 158.00 186.00 OK

Check Details

Version: 1.1.1
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘rpart’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-x86_64

Version: 1.1.1
Check: data for non-ASCII characters
Result: NOTE
     Note: found 9 marked UTF-8 strings
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64-gcc10-UCRT, r-patched-solaris-x86, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-x86_64

Version: 1.1.1
Check: examples
Result: FAIL
    Check process probably crashed or hung up for 20 minutes ... killed
    Most likely this happened in the example checks (?),
    if not, ignore the following last lines of example output:
    > set.seed(1) # to make the result of svm() reproducible.
    > svmfit = svm(y~.,data=data.frame(X=X,y=y),scale=FALSE,kernel="radial",
    + cost=10, gamma=1, probability=TRUE)
    > vcr.train = vcr.svm.train(X, y, svfit=svmfit)
     The kernel matrix has 4 eigenvalues below precS = 1e-12.
     Will take this into account.
    > # As "new" data we take a subset of the training data:
    > inds = c(1:25,101:125,151:175)
    > vcr.test = vcr.svm.newdata(X[inds,],y[inds],vcr.train)
    > plot(vcr.test$PAC,vcr.train$PAC[inds]); abline(0,1) # match
    > plot(vcr.test$farness,vcr.train$farness[inds]); abline(0,1)
    > confmat.vcr(vcr.test)
    
    Confusion matrix:
     predicted
    given blue red
     blue 46 4
     red 4 21
    
    The accuracy is 89.33%.
    > cols = c("deepskyblue3","red")
    > stackedplot(vcr.test, classCols = cols)
    > classmap(vcr.train, "blue", classCols = cols) # for comparison
    > classmap(vcr.test, "blue", classCols = cols)
    > classmap(vcr.train, "red", classCols = cols) # for comparison
    > classmap(vcr.test, "red", classCols = cols)
    >
    >
    > # For more examples, we refer to the vignettes:
    > vignette("Support_vector_machine_examples")
    ======== End of example output (where/before crash/hang up occured ?) ========
Flavor: r-devel-windows-x86_64