dirttee: Distributional Regression for Time to Event Data

Semiparametric distributional regression methods (expectile, quantile and mode regression) for time-to-event variables with right-censoring; uses inverse probability of censoring weights or accelerated failure time models with auxiliary likelihoods. Expectile regression using inverse probability of censoring weights has been introduced in Seipp et al. (2021) “Weighted Expectile Regression for Right-Censored Data” <doi:10.1002/sim.9137>, mode regression for time-to-event variables has been introduced in Seipp et al. (2022) “Flexible Semiparametric Mode Regression for Time-to-Event Data” <doi:10.1177/09622802221122406>.

Version:	1.0.2
Depends:	expectreg (≥ 0.5.0), R (≥ 3.6.0)
Imports:	formula.tools, MASS, Matrix, mgcv, nloptr, provenance, rlang, splines, survival
Published:	2023-12-21
DOI:	10.32614/CRAN.package.dirttee
Author:	Alexander Seipp [aut, cre], Fabian Otto-Sobotka [aut]
Maintainer:	Alexander Seipp <alexander.seipp at uni-oldenburg.de>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	dirttee results

Documentation:

Reference manual:

dirttee.pdf

Downloads:

Package source:	dirttee_1.0.2.tar.gz
Windows binaries:	r-devel: dirttee_1.0.2.zip, r-release: dirttee_1.0.2.zip, r-oldrel: dirttee_1.0.2.zip
macOS binaries:	r-release (arm64): dirttee_1.0.2.tgz, r-oldrel (arm64): dirttee_1.0.2.tgz, r-release (x86_64): dirttee_1.0.2.tgz, r-oldrel (x86_64): dirttee_1.0.2.tgz
Old sources:	dirttee archive

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