Hash an expression with its dependencies and store its
	value, reloading it from a file as long as both the expression and
	its dependencies stay the same.

Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) <doi:10.1002/bimj.201800391>.

Several statistical methods for analyzing survival data under various forms of dependent
    censoring are implemented in the package. In addition to accounting for dependent censoring, it 
    offers tools to adjust for unmeasured confounding factors. The implemented approaches allow 
    users to estimate the dependency between survival time and dependent censoring time, based 
    solely on observed survival data. For more details on the methods, refer to Deresa and Van 
    Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023)
    <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>,
    Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Willems et al. (2025) 
    <doi:10.48550/arXiv.2403.11860>, Ding and Van Keilegom (2025) and D'Haen et al. (2025)
    <doi:10.1007/s10985-025-09647-0>.

Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2021): <https://arfjournals.com/image/catalog/Journals%20Papers/AJSS/No%202%20(2021)/4-AJSS_123-150.pdf>; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140.