CRAN Package Check Results for Maintainer ‘Navdeep Gill <navdeep at h2o.ai>’

Last updated on 2025-10-21 19:52:08 CEST.

Package	NOTE
h2o4gpu	13

Package h2o4gpu

Current CRAN status:

Version: 0.3.3
Check: Rd files
Result: 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.} | ^ 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-macos-x86_64, r-oldrel-windows-x86_64

Version: 0.3.3
Check: HTML version of manual
Result: NOTE Found the following HTML validation problems: generics.html:54:1 (generics.Rd:15): Warning: missing </ul> before </div> h2o4gpu.elastic_net_classifier.html:167:1 (h2o4gpu.elastic_net_classifier.Rd:75): Warning: missing </ul> before </div> h2o4gpu.elastic_net_regressor.html:167:1 (h2o4gpu.elastic_net_regressor.Rd:75): Warning: missing </ul> before </div> h2o4gpu.gradient_boosting_classifier.html:202:1 (h2o4gpu.gradient_boosting_classifier.Rd:94): Warning: missing </ul> before </div> h2o4gpu.gradient_boosting_regressor.html:206:1 (h2o4gpu.gradient_boosting_regressor.Rd:96): Warning: missing </ul> before </div> h2o4gpu.kmeans.html:106:1 (h2o4gpu.kmeans.Rd:42): Warning: missing </ul> before </div> h2o4gpu.pca.html:85:1 (h2o4gpu.pca.Rd:31): Warning: missing </ul> before </div> h2o4gpu.random_forest_classifier.html:146:1 (h2o4gpu.random_forest_classifier.Rd:64): Warning: missing </ul> before </div> h2o4gpu.random_forest_regressor.html:142:1 (h2o4gpu.random_forest_regressor.Rd:62): Warning: missing </ul> before </div> h2o4gpu.truncated_svd.html:80:1 (h2o4gpu.truncated_svd.Rd:28): Warning: missing </ul> before </div> Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc