Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon 
    treatment protocol, is critical for realizing the benefits of prescription medications. 
    Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), 
    pill bottles or boxes that contain a computer chip that records the date and time of each 
    opening (or “actuation”). Before researchers can use EAMD data, they must apply a series of 
    decision rules to transform actuation data into adherence data. 
    The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, 
    information about the patient, regimen, and non-monitored periods into two daily adherence values -- 
    Dose Taken and Correct Dose Taken.

Bayesian logistic regression model with optional
  EXchangeability-NonEXchangeability parameter modelling for flexible
  borrowing from historical or concurrent data-sources. The safety model
  can guide dose-escalation decisions for adaptive oncology Phase I
  dose-escalation trials which involve an arbitrary number of
  drugs. Please refer to Neuenschwander et al. (2008)
  <doi:10.1002/sim.3230> and Neuenschwander et al. (2016)
  <doi:10.1080/19466315.2016.1174149> for details on the methodology.

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'.