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rstpm2
View on CRAN: Click
here
Download and install rstpm2 package within the R console
Install from CRAN:
install.packages("rstpm2")
Install from Github:
library("remotes")
install_github("cran/rstpm2")
Install by package version:
library("remotes")
install_version("rstpm2", "1.6.6.1")
Attach the package and use:
library("rstpm2")
Maintained by
Mark Clements
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.rstpm2 . https://github.com/mclements/rstpm2/issues . https://github.com/mclements/rstpm2 . rstpm2 citation info . rstpm2 results . rstpm2.pdf . Introduction to the rstpm2 Package . exttt{rstpm2}: a simple guide . rstpm2_1.6.6.1.tar.gz . rstpm2_1.6.6.1.zip . rstpm2_1.6.6.1.zip . rstpm2_1.6.6.1.zip . rstpm2_1.6.6.1.tgz . rstpm2_1.6.6.1.tgz . rstpm2_1.6.6.1.tgz . rstpm2_1.6.6.1.tgz . rstpm2_1.6.6.1.tgz . rstpm2_1.6.6.1.tgz . rstpm2 archive . https://CRAN.R-project.org/package=rstpm2 .
First Published: 2015-07-26
Latest Update: 2023-12-05
Description:
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
How to cite:
Mark Clements (2015). rstpm2: Smooth Survival Models, Including Generalized Survival Models. R package version 1.6.6.1, https://cran.r-project.org/web/packages/rstpm2. Accessed 27 Mar. 2025.
Previous versions and publish date:
1.2.2 (2015-07-26 18:57), 1.3.1 (2016-02-24 16:59), 1.3.2 (2016-04-13 17:59), 1.3.4 (2016-10-09 09:19), 1.4.0 (2017-08-31 23:37), 1.4.1 (2017-09-20 20:30), 1.4.2 (2018-05-29 15:45), 1.4.4 (2018-11-01 23:30), 1.4.5 (2019-01-17 16:50), 1.5.0 (2019-10-15 17:10), 1.5.1 (2019-11-06 00:00), 1.5.2 (2021-03-03 18:10), 1.5.5 (2022-04-19 10:10), 1.5.6 (2022-05-10 14:30), 1.5.7 (2022-07-14 00:50), 1.5.8 (2022-10-17 16:50), 1.5.9 (2023-01-07 04:40), 1.6.1 (2023-02-28 17:02), 1.6.2 (2023-03-08 14:20), 1.6.3 (2023-12-05 16:30), 1.6.4 (2024-08-19 15:40), 1.6.5 (2024-08-20 12:00), 1.6.6 (2024-10-29 15:20)
Other packages that cited rstpm2 R package
View rstpm2 citation profile
Other R packages that rstpm2 depends,
imports, suggests or enhances
Complete documentation for rstpm2
Functions, R codes and Examples using
the rstpm2 R package
Some associated functions: aft-class . aft . bhazard . brcancer . coef . colon . cox.tvc . eform . grad . gsm.control . gsm . gsm_design . incrVar . legendre.quadrature.rule.200 . lines . markov_msm . markov_sde . nsx . nsxD . numDeltaMethod . plot-methods . popmort . predict-methods . predict.nsx . predictnl-methods . predictnl . pstpm2-class . residuals-methods . rstpm2-internal . simulate-methods . smoothpwc . stpm2-class . tvcCoxph-class . update-methods . voptimize . vuniroot .
Some associated R codes: RcppExports.R . aft.R . gaussquad.R . multistate.R . pm2-3.R . random.R . read_gsm.R . tvc.coxph.R . vuniroot.R . zzz.R . Full rstpm2 package functions and examples
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