Piece-Wise Exponential Additive Mixed Modeling Tools
c(
person("Andreas", "Bender", , "andreas.bender@stat.uni-muenchen.de", role = c("aut", "cre"), comment=c(ORCID = "0000-0001-5628-8611")),
person("Fabian", "Scheipl", , "fabian.scheipl@stat.uni-muenchen.de", role = c("aut")))
The Piece-wise exponential (Additive Mixed) Model
(PAMM; Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>) is a
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
functions as well as visualization.
mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
(>= 0.7.0), ggplot2, dplyr (>= 0.7.0), purrr (>= 0.2.3),
tibble, msm, lazyeval, Formula, mvtnorm
RColorBrewer, scam, coxme, testthat, timereg, knitr,
rmarkdown, grid, gridExtra, covr, pkgdown
Andreas Bender [aut, cre] (<https://orcid.org/0000-0001-5628-8611>),
Fabian Scheipl [aut]

Piece-Wise Exponential Additive Mixed Modeling Tools for
Survival Analysis
The Piece-wise exponential (Additive Mixed) Model
(PAMM; Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>) is a
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>.
pammtools provides tidy workflow for survival analysis with PAMMs,
including data simulation, transformation and other functions for data
preprocessing and model post-processing as well as visualization.
mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
(>= 0.8.3), ggplot2, dplyr (>= 0.7.0), purrr (>= 0.2.3),
tibble, msm, lazyeval, Formula, mvtnorm