Package: BioPred 1.0.2

Zihuan Liu

BioPred: An R Package for Biomarkers Analysis in Precision Medicine

Provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Authors:Zihuan Liu [aut, cre], Yan Sun [aut], Xin Huang [aut]

BioPred_1.0.2.tar.gz
BioPred_1.0.2.tar.gz(r-4.5-noble)BioPred_1.0.2.tar.gz(r-4.4-noble)
BioPred_1.0.2.tgz(r-4.4-emscripten)BioPred_1.0.2.tgz(r-4.3-emscripten)
BioPred.pdf |BioPred.html✨
BioPred/json (API)

# Install 'BioPred' in R:

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

Datasets:

tutorial_data - Tutorial Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 630 downloads 21 exports 103 dependencies

Last updated 5 months agofrom:4069efa5d7. Checks:3 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 05 2025
R-4.5-linux	OK	Mar 05 2025
R-4.4-linux	OK	Mar 05 2025

Exports:cat_summary cdf_plot cut_perf eval_metric_bin eval_metric_con eval_metric_sur fixcut_bin fixcut_con fixcut_sur gam_ctr_plot gam_plot get_subgroup_results predictive_biomarker_imp roc_bin roc_bin_plot scat_cont_plot subgrp_perf subgrp_perf_pred XGBoostSub_bin XGBoostSub_con XGBoostSub_sur

Dependencies:abind backports boot broom car carData cli colorspace commonmark corrplot cowplot cpp11 curl data.table Deriv doBy dplyr evaluate exactRankTests fansi farver Formula generics ggplot2 ggpubr ggrepel ggsci ggsignif ggtext glue gridExtra gridtext gtable highr isoband jpeg jsonlite km.ci KMsurv knitr labeling lattice lifecycle lme4 magrittr markdown MASS Matrix MatrixModels maxstat mgcv microbenchmark minqa modelr moments munsell mvtnorm nlme nloptr nnet nortest numDeriv onewaytests pbkrtest pillar pkgconfig plyr png polynom pROC PropCIs purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppEigen Rdpack reformulas rlang rstatix scales SparseM stringi stringr survival survminer survMisc tibble tidyr tidyselect utf8 vctrs viridisLite wesanderson withr xfun xgboost xml2 xtable yaml zoo

BioPred Package Tutorial

Zihuan Liu, 11/2024

Rendered fromTutorial.Rmdusingknitr::rmarkdownon Mar 05 2025.

Last update: 2024-11-04
Started: 2024-06-07

Citation

Readme and manuals

Help Manual

Help page	Topics
Summarize Categorical Variables in Subgroup	cat_summary
CDF Plot for a biomarker	cdf_plot
Cutoff Performance Evaluation	cut_perf
Evaluation Metrics for XGBoostSub_bin Model	eval_metric_bin
Evaluation Metrics for XGBoostSub_con Model	eval_metric_con
Evaluation Metrics for XGBoostSub_sur Model	eval_metric_sur
Fixed Cutoff Analysis for Individual Biomarker Associated with Binary Outcome Variables	fixcut_bin
Fixed Cutoff Analysis for Individual Biomarker Associated with Continuous Outcome	fixcut_con
Fixed Cutoff Analysis for Individual Biomarker Associated with Survival Outcome	fixcut_sur
GAM Contrast Plot	gam_ctr_plot
GAM Plot	gam_plot
Get Subgroup Results	get_subgroup_results
Plot Predictive Biomarker Importance based on XGBoost-based Subgroup Model	predictive_biomarker_imp
AUC ROC Table for Biomarkers Associated with Binary Outcomes	roc_bin
ROC Plot Biomarkers Associated with Binary Outcomes	roc_bin_plot
Scatter Plot for a Biomarker Associated with Continuous Outcome	scat_cont_plot
Subgroup Performance Evaluation for Prognostic Cases	subgrp_perf
Subgroup Performance Evaluation for Predictive Cases	subgrp_perf_pred
Tutorial Data	tutorial_data
XGBoost Model with Modified Loss Function for Subgroup Identification with Binary Outcomes	XGBoostSub_bin
XGBoost Model with Modified Loss Function for Subgroup Identification with Continuous Outcomes	XGBoostSub_con
XGBoost Model with Modified Loss Function for Subgroup Identification with Survival Outcomes	XGBoostSub_sur