* using log directory 'd:/Rcompile/CRANpkg/local/4.4/h2o4gpu.Rcheck'
* using R version 4.4.2 (2024-10-31 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 13.3.0
    GNU Fortran (GCC) 13.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* checking for file 'h2o4gpu/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'h2o4gpu' version '0.3.3'
* package encoding: UTF-8
* 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 hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'h2o4gpu' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [2s] OK
* checking whether the package can be loaded with stated dependencies ... [2s] OK
* checking whether the package can be unloaded cleanly ... [2s] OK
* checking whether the namespace can be loaded with stated dependencies ... [2s] OK
* checking whether the namespace can be unloaded cleanly ... [2s] OK
* checking loading without being on the library search path ... [2s] OK
* checking whether startup messages can be suppressed ... [2s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [5s] OK
* checking Rd files ... [1s] NOTE
checkRd: (-1) h2o4gpu.gradient_boosting_classifier.Rd:62: Lost braces; missing escapes or markup?
    62 | \item{tree_method}{The tree construction algorithm used in XGBoost Distributed and external memory version only support approximate algorithm. Choices: {‘auto’, ‘exact’, ‘approx’, ‘hist’, ‘gpu_exact’, ‘gpu_hist’} ‘auto’: Use heuristic to choose faster one. - For small to medium dataset, exact greedy will be used. - For very large-dataset, approximate algorithm will be chosen. - Because old behavior is always use exact greedy in single machine, - user will get a message when approximate algorithm is chosen to notify this choice. ‘exact’: Exact greedy algorithm. ‘approx’: Approximate greedy algorithm using sketching and histogram. ‘hist’: Fast histogram optimized approximate greedy algorithm. It uses some performance improvements such as bins caching. ‘gpu_exact’: GPU implementation of exact algorithm. ‘gpu_hist’: GPU implementation of hist algorithm.}
       |                                                                                                                                                         ^
checkRd: (-1) h2o4gpu.gradient_boosting_regressor.Rd:64: Lost braces; missing escapes or markup?
    64 | \item{tree_method}{The tree construction algorithm used in XGBoost Distributed and external memory version only support approximate algorithm. Choices: {‘auto’, ‘exact’, ‘approx’, ‘hist’, ‘gpu_exact’, ‘gpu_hist’} ‘auto’: Use heuristic to choose faster one. - For small to medium dataset, exact greedy will be used. - For very large-dataset, approximate algorithm will be chosen. - Because old behavior is always use exact greedy in single machine, - user will get a message when approximate algorithm is chosen to notify this choice. ‘exact’: Exact greedy algorithm. ‘approx’: Approximate greedy algorithm using sketching and histogram. ‘hist’: Fast histogram optimized approximate greedy algorithm. It uses some performance improvements such as bins caching. ‘gpu_exact’: GPU implementation of exact algorithm. ‘gpu_hist’: GPU implementation of hist algorithm.}
       |                                                                                                                                                         ^
checkRd: (-1) h2o4gpu.random_forest_classifier.Rd:58: Lost braces; missing escapes or markup?
    58 | \item{tree_method}{The tree construction algorithm used in XGBoost Distributed and external memory version only support approximate algorithm. Choices: {‘auto’, ‘exact’, ‘approx’, ‘hist’, ‘gpu_exact’, ‘gpu_hist’} ‘auto’: Use heuristic to choose faster one. - For small to medium dataset, exact greedy will be used. - For very large-dataset, approximate algorithm will be chosen. - Because old behavior is always use exact greedy in single machine, - user will get a message when approximate algorithm is chosen to notify this choice. ‘exact’: Exact greedy algorithm. ‘approx’: Approximate greedy algorithm using sketching and histogram. ‘hist’: Fast histogram optimized approximate greedy algorithm. It uses some performance improvements such as bins caching. ‘gpu_exact’: GPU implementation of exact algorithm. ‘gpu_hist’: GPU implementation of hist algorithm.}
       |                                                                                                                                                         ^
checkRd: (-1) h2o4gpu.random_forest_regressor.Rd:56: Lost braces; missing escapes or markup?
    56 | \item{tree_method}{The tree construction algorithm used in XGBoost Distributed and external memory version only support approximate algorithm. Choices: {‘auto’, ‘exact’, ‘approx’, ‘hist’, ‘gpu_exact’, ‘gpu_hist’} ‘auto’: Use heuristic to choose faster one. - For small to medium dataset, exact greedy will be used. - For very large-dataset, approximate algorithm will be chosen. - Because old behavior is always use exact greedy in single machine, - user will get a message when approximate algorithm is chosen to notify this choice. ‘exact’: Exact greedy algorithm. ‘approx’: Approximate greedy algorithm using sketching and histogram. ‘hist’: Fast histogram optimized approximate greedy algorithm. It uses some performance improvements such as bins caching. ‘gpu_exact’: GPU implementation of exact algorithm. ‘gpu_hist’: GPU implementation of hist algorithm.}
       |                                                                                                                                                         ^
* 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 installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ... [2s] OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ... [34s] OK
  Running 'testthat.R' [34s]
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [3s] OK
* checking PDF version of manual ... [29s] OK
* checking HTML version of manual ... [3s] OK
* DONE
Status: 1 NOTE