CRAN Package Check Results for Package DoseFinding

Last updated on 2014-04-18 05:48:02.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9-11 1.75 47.18 48.94 OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang 0.9-11 93.67 OK
r-devel-linux-x86_64-fedora-gcc 0.9-11 89.70 OK
r-devel-macosx-x86_64-clang 0.9-11 78.87 OK
r-devel-macosx-x86_64-gcc 0.9-11 ERROR
r-devel-windows-ix86+x86_64 0.9-11 9.00 94.00 103.00 OK
r-patched-linux-x86_64 0.9-11 1.74 47.86 49.61 OK
r-patched-solaris-sparc 0.9-11 507.20 OK
r-patched-solaris-x86 0.9-11 105.80 OK
r-release-linux-ix86 0.9-11 4.00 107.00 111.00 OK
r-release-linux-x86_64 0.9-11 1.95 48.22 50.17 OK
r-release-macosx-x86_64 0.9-11 ERROR
r-release-windows-ix86+x86_64 0.9-11 8.00 82.00 90.00 ERROR
r-oldrel-windows-ix86+x86_64 0.9-11 9.00 87.00 96.00 ERROR

Check Details

Version: 0.9-11
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘multcomp’
Flavors: r-devel-macosx-x86_64-gcc, r-release-macosx-x86_64

Version: 0.9-11
Check: tests
Result: ERROR
    Running the tests in ‘tests/testsMCT.R’ failed.
    Last 13 lines of output:
     Type 'contributors()' for more information and
     'citation()' on how to cite R or R packages in publications.
    
     Type 'demo()' for some demos, 'help()' for on-line help, or
     'help.start()' for an HTML browser interface to help.
     Type 'q()' to quit R.
    
     > library(DoseFinding)
     Loading required package: lattice
     Loading required package: mvtnorm
     > library(multcomp)
     Error in library(multcomp) : there is no package called 'multcomp'
     Execution halted
Flavors: r-devel-macosx-x86_64-gcc, r-release-macosx-x86_64

Version: 0.9-11
Check: running tests for arch 'i386'
Result: ERROR
    Running the tests in 'tests/testsMCPMod.R' failed.
    Last 13 lines of output:
     + aa[sample(1:nrow(aa)),]
     + }
     >
     > #### simulate data
     > set.seed(10)
     > dd <- getDFdataSet()
     > bet <- guesst(0.9*max(dd$x), p=0.8, "betaMod", scal = 1.2*max(dd$x),
     + dMax = 0.7*max(dd$x), Maxd = max(dd$x))
     > sE <- guesst(c(0.5*max(dd$x), 0.7*max(dd$x)) , p=c(0.5, 0.9), "sigEmax")
     > models <- Mods(linear = NULL, betaMod = bet, sigEmax = sE,
     + doses = sort(unique(dd$x)),
     + addArgs=list(scal = 1.2*max(dd$x)))
     > obj <- MCPMod(x,y, dd, models=models, addCovars = ~cov1+cov2, alpha=0.05, Delta=0.5)
Flavor: r-release-windows-ix86+x86_64