Cellular cooperation compromises the plating efficiency-based 
    analysis of clonogenic survival data. This tool provides functions that 
    enable a robust analysis of colony formation assay (CFA) data in presence 
    or absence of cellular cooperation. 
    The implemented method has been described 
    in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. 
    "The clonogenic assay: robustness of plating efficiency-based analysis is 
    strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020).
    <doi:10.1186/s13014-020-01697-y>)
        Power regression for parameter estimation, calculation of survival
    fractions, uncertainty analysis and plotting functions are provided.

Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
  with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
  CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For 
  mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.

Package for the analysis of categorical functional data.
  The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>.
  It also provides functions to perform basic statistical analysis on categorical functional data.

Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.