Title: Parametric and semi-parametric analyses of semi-competing risks
Author: Kyu Ha Lee and Sebastien Haneuse
Maintainer: Kyu Ha Lee <klee@hsph.harvard.edu>
Description: In this package, we provide parametric and semi-parametric regression models to analyze semi-competing risks data.

Title: Parametric and semi-parametric analysis of semi-competing risks
Description: This package contains algorithms to perform various types of parametric and semi-parametric analyses of semi-competing risks data. We also provide functions to fit survival regression models to univariate time-to-event outcome.

Title: Hierarchical Models for Parametric and Semi-Parametric Analyses
Description: Parametric and semi-parametric analyses of semi-competing risks/univariate survival data.

Author: Kyu Ha Lee, Catherine Lee, and Sebastien Haneuse

Description: Parametric and semi-parametric analyses of semi-competing risks/univariate survival data. For semi-competing risks data, the package contains implementations of hierarchical models for independent data (Lee et al., 2015; <dio:10.1111/rssc.12078>) and cluster-correlated data (Lee et al., 2016; <dio:10.1080/01621459.2016.1164052>).

Description: Parametric and semi-parametric analyses of semi-competing risks/univariate survival data. For semi-competing risks data, the package contains implementations of hierarchical models for independent data (Lee et al., 2015; <doi:10.1111/rssc.12078>, Lee et al. 2017) and cluster-correlated data (Lee et al., 2016; <doi:10.1111/biom.12696> <doi:10.1080/01621459.2016.1164052>).

Author: Kyu Ha Lee, Catherine Lee, Danilo Alvares, and Sebastien Haneuse
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 para- metric models. The package also includes functions for univariate survival analysis as complementary analysis tools.