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The rmo-package provides efficient sampling algorithms for the Marshall–Olkin distribution and a flexible S4-class system for creating diverse parametrizations.

Sampling

Simulation algorithms are provided for various MO parametrizations. The semantic naming scheme r*mo is used, e.g.,

  • rpextmo() allows to simulate from parametric families of extendible Marshall–Olkin distributions. The function takes a killing-rate, a drift, a scaling factor, a parameter vector, and a family name as input.

  • rextmo() allows to simulate from extendible Marshall–Olkin distributions. It takes a Bernstein function as input.

  • rexmo() allows to simulate from exchangeable Marshall–Olkin distributions. It takes a vector of exchangeable shock-size arrival intensities as input.

  • rmo() allows to simulate from Marshall–Olkin distributions. It takes vector of shock arrival intensities as input and uses the Arnold model or exogenous shock model for sampling; the former can be used up until dimension \(30\), but the latter should only be used in very small dimensions.

The default simulation algorithm is the Markovian death-counting model. Dependent on the parametrization, other algorithms can be used, e.g., the exogenous shock model, the Arnold model, or the Lévy-frailty model.

Bernstein functions

A Bernstein function can be used to parametrize the extendible Marshall–Olkin distribution.

Author

Maintainer: Henrik Sloot henrik.sloot@gmail.com (ORCID)