Title: Piece-Wise Exponential Additive Mixed Modeling Tools
person("Fabian", "Scheipl", , "fabian.scheipl@stat.uni-muenchen.de", role = c("aut")))
Description: The Piece-wise exponential (Additive Mixed) Model
powerful model class for survival analysis, based on Generalized Additive
(Mixed) Models (GA(M)Ms). It offers intuitive specification and robust
estimation of complex survival models with stratified baseline hazards,
random effects, time-varying effects, time-dependent covariates and
cumulative effects (Bender et. al. (2018) <doi:10.1093/biostatistics/kxy003>.
pammtools provides tidy workflow for survival analysis with PAMMs,
including data transformation and other pre- and post-processing
Imports: mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
Suggests: RColorBrewer, scam, coxme, testthat, timereg, knitr,
rmarkdown, grid, gridExtra, covr, pkgdown

Title: Piece-Wise Exponential Additive Mixed Modeling Tools for
powerful model class for the analysis of survival (or time-to-event) data,
based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2018) <doi:10.1093/biostatistics/kxy003>.
including data simulation, transformation and other functions for data
preprocessing and model post-processing as well as visualization.

Description: Functions that facilitate fitting
piece-wise exponential (additive mixed) models (Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>).
This includes restructuring the data to the needed format and various
convenience functions, e.g., for plotting the results.

based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2018) <doi: 10.1093/biostatistics/kxy003>.