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.
RColorBrewer, scam, coxme, testthat, timereg, knitr,
rmarkdown, grid, gridExtra, covr

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
(>= 1.0.0), ggplot2, dplyr (>= 0.7.0), purrr (>= 0.2.3),
tibble, lazyeval, Formula, mvtnorm, pec

mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
(>= 1.0.0), ggplot2 (>= 3.2.0), dplyr (>= 0.7.0), purrr (>=
0.2.3), tibble, lazyeval, Formula, mvtnorm, pec

mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
(>= 1.0.0), ggplot2 (>= 3.2.0), dplyr (>= 0.7.0), purrr (>=
0.2.3), tibble, lazyeval, Formula, mvtnorm, pec, vctrs

mgcv, survival (>= 2.39-5), checkmate, magrittr, rlang, tidyr
(>= 1.0.0), ggplot2 (>= 3.2.0), dplyr (>= 1.0.0), purrr (>=
0.2.3), tibble, lazyeval, Formula, mvtnorm, pec, vctrs (>=
0.3.0)

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"), comment = c(ORCID = "0000-0001-8172-3603")),
person("Philipp", "Kopper", , "philipp.kopper@stat.uni-muenchen.de", role = c("aut"), comment = c(ORCID="0000-0002-5037-7135")))
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>, as well as support for left-truncated, competing risks and recurrent events data.
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
(>= 1.0.0), ggplot2 (>= 3.2.2), dplyr (>= 1.0.0), purrr (>=
0.2.3), tibble, lazyeval, Formula, mvtnorm, pec, vctrs (>=
0.3.0)