pammtools: Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (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 (2019)), 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.

Version:	0.5.93
Depends:	R (≥ 3.5.0)
Imports:	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)
Suggests:	testthat
Published:	2024-02-25
DOI:	10.32614/CRAN.package.pammtools
Author:	Andreas Bender [aut, cre], Fabian Scheipl [aut], Philipp Kopper [aut], Lukas Burk [ctb]
Maintainer:	Andreas Bender <andreas.bender at stat.uni-muenchen.de>
BugReports:	https://github.com/adibender/pammtools/issues
License:	MIT + file LICENSE
URL:	https://adibender.github.io/pammtools/
NeedsCompilation:	no
Citation:	pammtools citation info
Materials:	README NEWS
CRAN checks:	pammtools results

Documentation:

Reference manual:

pammtools.pdf

Downloads:

Package source:	pammtools_0.5.93.tar.gz
Windows binaries:	r-devel: pammtools_0.5.93.zip, r-release: pammtools_0.5.93.zip, r-oldrel: pammtools_0.5.93.zip
macOS binaries:	r-release (arm64): pammtools_0.5.93.tgz, r-oldrel (arm64): pammtools_0.5.93.tgz, r-release (x86_64): pammtools_0.5.93.tgz, r-oldrel (x86_64): pammtools_0.5.93.tgz
Old sources:	pammtools archive

Reverse dependencies:

Reverse imports:	autoReg, KMunicate, relsurv, robber
Reverse suggests:	adjustedCurves, contsurvplot

Linking:

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