Title: Predictive Evaluation Metrics in Survival Analysis
person("Hanpu", "Zhou", role = c("aut", "cre"), email = "zhouhanpu@csu.edu.cn"),
person("Xuewei", "Cheng", role = c("aut"), email = "xwcheng@csu.edu.cn"),
person("Sizheng", "Wang", role = c("aut"), email = "964555106@qq.com"),
person("Hong", "Wang", role = c("aut"), email = "wh@csu.edu.cn")
Description: An implementation of popular evaluation metrics that are commonly used in survival prediction
including Concordance Index, Brier Score, Integrated Brier Score,
Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and
(Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.
Imports: survminer, stats, survival, MASS
Suggests: knitr, rmarkdown, randomForestSRC, caret, ggplot2, ggpubr,

Suggests: knitr, rmarkdown, caret, ggplot2, ggpubr, pec,

Imports: survminer, stats, survival, MASS, pec, randomForestSRC
Suggests: knitr, rmarkdown, caret, ggplot2, ggpubr, testthat (>= 3.0.0)

Authors@R: c(person("Michael", "Laimighofer", role = c("aut", "cre","ctb"),
Author: Michael Laimighofer [aut, cre, ctb]
Description: Estimation of the prediction accuracy in a unified survival AUC
approach. Model selection and prediction estimation based on a survival AUC.
Stepwise model selection, based on several ranking approaches.
Packaged: 2015-08-26 09:57:55 UTC; michael.laimighofer

Title: Weibull Regression for a Right-Censored Endpoint with an
Author: Stanislas Hubeaux <stanislas.hubeaux@epfl.ch> and Kaspar Rufibach <kaspar.rufibach@gmail.com>
Description: The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one arbitrarily censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.