Description: Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
Depends: MASS, survival, Formula, R (>= 3.3.0)

Maintainer: Kyu Ha Lee <klee15239@gmail.com>

Title: Solve Semi-Parametric Estimation by Implicit Profiling
Description: Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.
Suggests: knitr, rmarkdown, numDeriv, purrr, rlang, testthat (>=
Author: Jinhua Su [aut, cre] (<https://orcid.org/0000-0002-6344-0607>)

Author: Agnieszka Listwon, Philippe Saint-Pierre
Description: Functions for fitting multi-state semi-Markov models to
longitudinal data. A parametric maximum likelihood estimation
method adapted to deal with Exponential, Weibull and
Exponentiated Weibull distributions is considered.
Right-censoring can be taken into account and both constant and
time-varying covariates can be included using a Cox

Author: Agnieszka Listwon-Krol, Philippe Saint-Pierre
Description: Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model.