Check results for 'CoxBoost'

using R version 3.6.0 beta (2019-04-15 r76395)
using platform: x86_64-apple-darwin15.6.0 (64-bit)
using session charset: UTF-8
checking for file ‘CoxBoost/DESCRIPTION’ ... OK
this is package ‘CoxBoost’ version ‘1.4’
checking package namespace information ... OK
checking package dependencies ... OK
checking if this is a source package ... OK
checking if there is a namespace ... OK
checking for executable files ... OK
checking for hidden files and directories ... OK
checking for portable file names ... OK
checking for sufficient/correct file permissions ... OK
checking whether package ‘CoxBoost’ can be installed ... [12s/19s] OK
checking installed package size ... OK
checking package directory ... OK
checking DESCRIPTION meta-information ... NOTE
Malformed Description field: should contain one or more complete sentences.
checking top-level files ... OK
checking for left-over files ... OK
checking index information ... OK
checking package subdirectories ... OK
checking R files for non-ASCII characters ... OK
checking R files for syntax errors ... OK
checking whether the package can be loaded ... OK
checking whether the package can be loaded with stated dependencies ... OK
checking whether the package can be unloaded cleanly ... OK
checking whether the namespace can be loaded with stated dependencies ... OK
checking whether the namespace can be unloaded cleanly ... OK
checking loading without being on the library search path ... OK
checking dependencies in R code ... NOTE
'library' or 'require' calls in package code:
  ‘parallel’ ‘snowfall’
  Please use :: or requireNamespace() instead.
  See section 'Suggested packages' in the 'Writing R Extensions' manual.
Packages in Depends field not imported from:
  ‘Matrix’ ‘prodlim’
  These packages need to be imported from (in the NAMESPACE file)
  for when this namespace is loaded but not attached.
checking S3 generic/method consistency ... OK
checking replacement functions ... OK
checking foreign function calls ... NOTE
Calls with DUP:
   .C("find_best01", x.double.vec, as.integer(n), as.integer(p),
       uncens.C, as.integer(length(uncens)), as.double(actual.beta),
       as.double(actual.risk.score), as.double(actual.linear.predictor),
       weight.double.vec, max.nz.vec, max.1.vec, as.double(weightmat.times.risk),
       as.double(weightmat.times.risk.sum), as.double(penalty),
       warncount = integer(1), min.index = integer(1), min.deviance = double(1),
       min.beta.delta = double(1), score.vec = double(p), DUP = FALSE,
       NAOK = TRUE)
   .C("find_best", x.double.vec, as.integer(n), as.integer(p), uncens.C,
       as.integer(length(uncens)), as.double(actual.beta), as.double(actual.risk.score),
       as.double(actual.linear.predictor), weight.double.vec, max.nz.vec,
       max.1.vec, as.double(weightmat.times.risk), as.double(weightmat.times.risk.sum),
       as.double(penalty), as.integer(criterion == "pscore" || criterion ==
           "hpscore"), warncount = integer(1), min.index = integer(1),
       min.deviance = double(1), min.beta.delta = double(1), score.vec = double(p),
       DUP = FALSE, NAOK = TRUE)
   .C("find_best_candidate", x.double.vec, as.integer(n), as.integer(p),
       uncens.C, as.integer(length(uncens)), as.double(actual.beta),
       as.double(actual.risk.score), as.double(actual.linear.predictor),
       weight.double.vec, max.nz.vec, max.1.vec, as.double(weightmat.times.risk),
       as.double(weightmat.times.risk.sum), as.double(penalty),
       as.integer(criterion == "pscore" || criterion == "hpscore"),
       as.integer(presel.index - 1), as.integer(length(presel.index)),
       warncount = integer(1), min.index = integer(1), min.deviance = double(1),
       min.beta.delta = double(1), score.vec = double(p), DUP = FALSE,
       NAOK = TRUE)
   .C("find_best_candidate", x.double.vec, as.integer(n), as.integer(p),
       uncens.C, as.integer(length(uncens)), as.double(actual.beta),
       as.double(actual.risk.score), as.double(actual.linear.predictor),
       weight.double.vec, max.nz.vec, max.1.vec, as.double(weightmat.times.risk),
       as.double(weightmat.times.risk.sum), as.double(penalty),
       as.integer(criterion == "pscore"), as.integer(new.candidates -
           1), as.integer(length(new.candidates)), warncount = integer(1),
       min.index = integer(1), min.deviance = double(1), min.beta.delta = double(1),
       score.vec = double(p), DUP = FALSE, NAOK = TRUE)
   .C("get_I_vec", as.double(x[subset.time.order, pen.index[I.index]]),
       as.integer(n), as.integer(length(I.index)), as.integer(length(uncens)),
       as.double(weightmat.times.risk), as.double(weightmat.times.risk.sum),
       I.vec = double(length(I.index)), DUP = FALSE)
DUP is no longer supported and will be ignored.
checking R code for possible problems ... [9s/12s] NOTE
CoxBoost: no visible global function definition for ‘Matrix’
CoxBoost: no visible binding for global variable ‘sd’
CoxBoost: no visible global function definition for ‘predict’
cv.CoxBoost : eval.fold: no visible global function definition for
  ‘predict’
cv.CoxBoost: no visible global function definition for ‘mclapply’
cv.CoxBoost: no visible binding for global variable ‘sd’
estimPVal: no visible global function definition for ‘mclapply’
iCoxBoost: no visible global function definition for ‘terms’
iCoxBoost: no visible global function definition for ‘model.response’
iCoxBoost: no visible global function definition for ‘model.frame’
iCoxBoost: no visible global function definition for ‘model.matrix’
iCoxBoost: no visible global function definition for ‘Matrix’
plot.CoxBoost: no visible global function definition for ‘plot’
plot.CoxBoost: no visible global function definition for ‘lines’
plot.CoxBoost: no visible global function definition for ‘text’
predict.iCoxBoost: no visible global function definition for
  ‘model.response’
predict.iCoxBoost: no visible global function definition for
  ‘model.frame’
predict.iCoxBoost: no visible global function definition for
  ‘model.matrix’
Undefined global functions or variables:
  Matrix lines mclapply model.frame model.matrix model.response plot
  predict sd terms text
Consider adding
  importFrom("graphics", "lines", "plot", "text")
  importFrom("stats", "model.frame", "model.matrix", "model.response",
             "predict", "sd", "terms")
to your NAMESPACE file.
checking Rd files ... OK
checking Rd metadata ... OK
checking Rd cross-references ... OK
checking for missing documentation entries ... OK
checking for code/documentation mismatches ... OK
checking Rd \usage sections ... OK
checking Rd contents ... OK
checking for unstated dependencies in examples ... OK
checking line endings in C/C++/Fortran sources/headers ... OK
checking compiled code ... OK
checking examples ... [8s/12s] OK
checking PDF version of manual ... OK
DONE
Status: 4 NOTEs