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'.

Implements methods to fit a parametric Bayesian multi-state model
    to tumor response data.
    The model can be used to sample from the predictive distribution to impute
    missing data and calculate probability of success for custom decision
    criteria in early clinical trials during an ongoing trial.
    The inference is implemented using 'stan'.

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.