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'.

Allows for building drug response models using screening data between bulk RNA-Seq and a drug response metric and two additional tools for biomarker discovery that have been developed by the Huang Laboratory at University of Minnesota. 
    There are 3 main functions within this package. 
    (1) calcPhenotype is used to build drug response models on RNA-Seq data and impute them on any other RNA-Seq dataset given to the model. 
    (2) GLDS is used to calculate the general level of drug sensitivity, which can improve biomarker discovery.
    (3) IDWAS can take the results from calcPhenotype and link the imputed response back to available genomic (mutation and CNV alterations) to identify biomarkers. 
    Each of these functions comes from a paper from the Huang research laboratory. Below gives the relevant paper for each function. 
    calcPhenotype - Geeleher et al, Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.
    GLDS - Geeleher et al, Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.
    IDWAS -  Geeleher et al, Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.