Package: ranktreeEnsemble 0.21

Min Lu

ranktreeEnsemble: Ensemble Models of Rank-Based Trees with Extracted Decision Rules

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

Authors:Ruijie Yin [aut], Chen Ye [aut], Min Lu [aut, cre]

# Install 'ranktreeEnsemble' in R:

install.packages('ranktreeEnsemble', repos = c('https://transbioinfolab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/transbioinfolab/ranktreeensemble/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

tnbc - Gene expression profiles in triple-negative breast cancer cell

On CRAN:

7 exports 0.84 score 71 dependencies 4 scripts 214 downloads

Last updated 4 months agofrom:277784d67b. Checks:OK: 1 WARNING: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 13 2024
R-4.5-win-x86_64	WARNING	Sep 13 2024
R-4.5-linux-x86_64	WARNING	Sep 13 2024
R-4.4-win-x86_64	WARNING	Sep 13 2024
R-4.4-mac-x86_64	WARNING	Sep 13 2024
R-4.4-mac-aarch64	WARNING	Sep 13 2024
R-4.3-win-x86_64	WARNING	Sep 13 2024
R-4.3-mac-x86_64	WARNING	Sep 13 2024
R-4.3-mac-aarch64	WARNING	Sep 13 2024

Exports:extract.rules importance pair predict rboost rforest select.rules

Dependencies:base64enc bit bit64 bslib cachem cli clipr colorspace cpp11 crayon data.tree DiagrammeR digest dplyr evaluate fansi farver fastmap fontawesome fs gbm generics glue highr hms htmltools htmlwidgets igraph jquerylib jsonlite knitr labeling lattice lifecycle magrittr Matrix memoise mime munsell pillar pkgconfig prettyunits progress purrr R6 randomForestSRC rappdirs RColorBrewer Rcpp readr rlang rmarkdown rstudioapi sass scales stringi stringr survival tibble tidyr tidyselect tinytex tzdb utf8 vctrs viridisLite visNetwork vroom withr xfun yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Extract Interpretable Decision Rules from a Random Forest Model	extract.rules
Variable Importance Index for Each Predictor	importance
Transform Continuous Variables into Ranked Binary Pairs	pair
Prediction or Extract Predicted Values for Random Forest, Random Forest Rule or Boosting Models	predict
Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules	ranktreeEnsemble
Generalized Boosted Modeling via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles	rboost
Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles	rforest rforest.tree
Select Decision Rules to Achieve Higher Prediction Accuracy	select.rules
Gene expression profiles in triple-negative breast cancer cell	tnbc