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Provides a function for estimating the transition probabilities in an illness-death model. The transition probabilities can be estimated from the unsmoothed landmark estimators developed by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>. Presmoothed estimates can also be obtained through the use of a parametric family of binary regression curves, such as logit, probit or cauchit. The additive logistic regression model and nonparametric regression are also alternatives which have been implemented. The idea behind the presmoothed landmark estimators is to use the presmoothing techniques developed by Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the landmark estimation of the transition probabilities.
Version: | 1.1.0 |
Depends: | R (≥ 3.0.0) |
Imports: | survPresmooth, mgcv |
Published: | 2019-11-01 |
DOI: | 10.32614/CRAN.package.presmTP |
Author: | Gustavo Soutinho, Luis Meira-Machado and Pedro Oliveira |
Maintainer: | Gustavo Soutinho <gustavosoutinho at sapo.pt> |
License: | GPL-3 |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | presmTP results |
Reference manual: | presmTP.pdf |
Package source: | presmTP_1.1.0.tar.gz |
Windows binaries: | r-devel: presmTP_1.1.0.zip, r-release: presmTP_1.1.0.zip, r-oldrel: presmTP_1.1.0.zip |
macOS binaries: | r-release (arm64): presmTP_1.1.0.tgz, r-oldrel (arm64): presmTP_1.1.0.tgz, r-release (x86_64): presmTP_1.1.0.tgz, r-oldrel (x86_64): presmTP_1.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.