Analysis and visualization tools using the BioPred R package. (A) Subgroup and predictive biomarker identification: this panel illustrates the use of several key functions-XGBoostSub_con(), XGBoostSub_bin(), XGBoostSub_sur(), eval_metric_con(), eval_metric_bin(), eval_metric_sur(), get_subgroup_results(), and predictive_biomarker_imp()-to identify subgroups and predictive biomarkers. The input is an R DataFrame, with the output including predicted subgroup results and a ranked list of biomarker importance. (B) Performance evaluation and visualization: this component demonstrates performance evaluation and visualization, utilizing functions such as cat_summary(), subgrp_perf(), subgrp_perf_pred(), fixcut_bin(), fixcut_con(), fixcut_sur(), cdf.plot(), gam_plot(), gam_ctr_plot(), cut_perf(), roc_bin_plot(), roc_bin(), and scat_cont_plot(). The input for this component comprises results from (A), with these functions providing insights into the strength of the identified biomarkers' association with outcomes. “Biomarker +” denotes the biomarker-positive subpopulation, where the biomarker value meets the cutoff or other criteria, while “Biomarker –” signifies the biomarker-negative subpopulation, where the biomarker value does not meet the respective criteria.