Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. 
    Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. 
    It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.

Aids in the analysis of genes influencing cancer survival by including a principal function, calculator(), which calculates the P-value for each provided gene under the optimal cutoff in cancer survival studies. Grounded in methodologies from significant works, this package references Therneau's 'survival' package (Therneau, 2024; <https://CRAN.R-project.org/package=survival>) and the survival analysis extensions by Therneau and Grambsch (2000, ISBN 0-387-98784-3). It also integrates the 'survminer' package by Kassambara et al. (2021; <https://CRAN.R-project.org/package=survminer>), enhancing survival curve visualizations with 'ggplot2'.

Provide functionality for cancer subtyping using nearest centroids or machine learning methods based on TCGA data.

Construct and evaluate directed tree structures that
   model the process of occurrence of genetic alterations during carcinogenesis
   as described in Szabo, A. and Boucher, K (2002)  <doi:10.1016/S0025-5564(02)00086-X>.

Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.

Two-stage design for single-arm phase II 
		trials with time-to-event endpoints (e.g., clinical trials on 
		immunotherapies among cancer patients) can be calculated using this package. 
		Two notable advantages of the package: 1) It provides flexible choices from 
		three design methods (optimal, minmax, and admissible), and 2) the power of
		the design is more accurately calculated using the exact variance in the
		one-sample log-rank test. The package can be used for 1) planning the sample
		sizes and other design parameters, and 2) conducting the interim and final
		analyses for the Go/No-go decisions. More details about the design method
		can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020).
		<doi:10.1002/pst.1983>.