CRAN Package Check Results for Package RandPro

Last updated on 2024-03-28 23:55:16 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.2.2 14.81 128.11 142.92 NOTE
r-devel-linux-x86_64-debian-gcc 0.2.2 11.70 96.54 108.24 NOTE
r-devel-linux-x86_64-fedora-clang 0.2.2 179.38 NOTE
r-devel-linux-x86_64-fedora-gcc 0.2.2 169.27 NOTE
r-devel-windows-x86_64 0.2.2 14.00 104.00 118.00 ERROR
r-patched-linux-x86_64 0.2.2 11.85 121.01 132.86 NOTE
r-release-linux-x86_64 0.2.2 9.74 124.22 133.96 NOTE
r-release-macos-arm64 0.2.2 53.00 NOTE
r-release-macos-x86_64 0.2.2 90.00 NOTE
r-release-windows-x86_64 0.2.2 18.00 127.00 145.00 NOTE
r-oldrel-macos-arm64 0.2.2 52.00 NOTE
r-oldrel-windows-x86_64 0.2.2 19.00 129.00 148.00 NOTE

Check Details

Version: 0.2.2
Check: LazyData
Result: NOTE 'LazyData' is specified without a 'data' directory Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-windows-x86_64

Version: 0.2.2
Check: examples
Result: ERROR Running examples in 'RandPro-Ex.R' failed The error most likely occurred in: > ### Name: classify > ### Title: Classification Function > ### Aliases: classify > ### Keywords: classification confusion_matrix feature_extraction k-nn svm > > ### ** Examples > > # Load Library > library(RandPro) > > #Load Iris Data > data("iris") > > #Split the data into training set and test set of 75:25 ratio. > set.seed(101) > sample <- sample.int(n = nrow(iris), size = floor(.75*nrow(iris)), replace = FALSE) > trainn <- iris[sample, ] > testt <- iris[-sample,] > > #Extract the train label and test label > trainl <- trainn$Species > testl <- testt$Species > typeof(trainl) [1] "integer" > > #Remove the label from training set and test set > trainn <- trainn[,1:4] > testt <- testt[,1:4] > > #classify the Iris data with default K-NN Classifier. > res <- classify(trainn,testt,trainl,testl) Function uses default value 0.5 for epsilon Function uses Gaussian Projection function Function uses default K-NN classifier Flavor: r-devel-windows-x86_64