smooth, doMC, doParallel, foreach, testthat, rmarkdown, knitr

smooth (>= 2.5.1), doMC, doParallel, foreach, testthat,
rmarkdown, knitr

forecast, stats, graphics, utils, lamW, numDeriv, nloptr,
statmod

c(person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK"),
person("Yves R.", "Sagaert", role = c("ctb"),
comment="Visiting Research at Centre for Marketing Analytics and Forecasting, Lancaster University, UK"))
Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
and Forecasting, Lancaster University, UK),
Yves R. Sagaert [ctb] (Visiting Research at Centre for Marketing
Analytics and Forecasting, Lancaster University, UK)

forecast, stats, graphics, utils, lamW, pracma, nloptr,
statmod

forecast, stats, graphics, utils, lamW, pracma, nloptr,
statmod, zoo, Matrix

forecast, stats, graphics, utils, lamW, pracma, nloptr,
statmod, gnorm, zoo, Matrix

forecast, stats, graphics, utils, lamW, pracma, nloptr,
statmod, zoo, Matrix, texreg, methods

smooth (>= 3.0.0), doMC, doParallel, foreach, testthat,
rmarkdown, knitr

stats, graphics, utils, lamW, pracma, nloptr, statmod, zoo,
Matrix, texreg, methods
smooth (>= 3.0.0), doMC, doParallel, foreach, testthat,
rmarkdown, forecast, knitr

stats, graphics, utils, lamW, pracma, nloptr, statmod, zoo,
Matrix, texreg, forecast, methods

stats, graphics, utils, lamW, pracma, nloptr, statmod, zoo,
texreg, methods

stats, generics (>= 0.1.2), graphics, utils, lamW, pracma,
nloptr, statmod, zoo, texreg, methods
smooth (>= 3.1.0), doMC, doParallel, foreach, testthat,
rmarkdown, knitr

stats, generics (>= 0.1.2), graphics, utils, pracma, nloptr,
statmod, zoo, texreg, methods

Fit a Grey-Zone Model with Survival Data
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.