R package timereg: Flexible regression models for survival data. Programs for Martinussen and Scheike (2006), ‘Dynamic Regression Models for Survival Data’, Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in ahaz package.

References in zbMATH (referenced in 82 articles , 1 standard article )

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  1. Sachs, M. C.; Gabriel, E. E.: Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R (2022) not zbMATH
  2. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  3. Kreiß, Alexander: Correlation bounds, mixing and (m)-dependence under random time-varying network distances with an application to Cox-processes (2021)
  4. Lin, Zhongjian; Liu, Ruixuan: A multiplex interdependent durations model (2021)
  5. Blanche, Paul: Confidence intervals for the cumulative incidence function via constrained NPMLE (2020)
  6. Heng, Fei; Sun, Yanqing; Hyun, Seunggeun; Gilbert, Peter B.: Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes (2020)
  7. Martinussen, Torben; Vansteelandt, Stijn; Andersen, Per Kragh: Subtleties in the interpretation of hazard contrasts (2020)
  8. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  9. Bischofberger, Stephan M.; Hiabu, Munir; Mammen, Enno; Nielsen, Jens Perch: A comparison of in-sample forecasting methods (2019)
  10. Blanche, Paul; Gerds, Thomas A.; Ekstrøm, Claus T.: The Wally plot approach to assess the calibration of clinical prediction models (2019)
  11. Eric S Kawaguchi, Jenny I Shen, Gang Li, Marc A Suchard: A Fast and Scalable Implementation Method for Competing Risks Data with the R Package fastcmprsk (2019) arXiv
  12. Liu, Lei; Shih, Ya-Chen Tina; Strawderman, Robert L.; Zhang, Daowen; Johnson, Bankole A.; Chai, Haitao: Statistical analysis of zero-inflated nonnegative continuous data: a review (2019)
  13. Lopez, Olivier: A censored copula model for micro-level claim reserving (2019)
  14. Lv, Xiaofeng; Zhang, Gupeng; Xu, Xinkuo; Li, Qinghai: Weighted quantile regression for censored data with application to export duration data (2019)
  15. Pavlič, Klemen; Martinussen, Torben; Andersen, Per Kragh: Goodness of fit tests for estimating equations based on pseudo-observations (2019)
  16. Sørensen, Ditte Nørbo; Martinussen, Torben; Tchetgen Tchetgen, Eric: A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting (2019)
  17. Bender, Andreas; Groll, Andreas; Scheipl, Fabian: A generalized additive model approach to time-to-event analysis (2018)
  18. Chen, Chyong-Mei; Shen, Pao-Sheng: Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data (2018)
  19. Liu, Wanrong; Fang, Jianglin; Lu, Xuewen: Additive-multiplicative hazards model with current status data (2018)
  20. Maxild Mortensen, Lotte; Hansen, Camilla Plambeck; Overvad, Kim; Lundbye-Christensen, Søren; Parner, Erik T.: The pseudo-observation analysis of time-to-event data. Example from the Danish Diet, Cancer and Health Cohort illustrating assumptions, model validation and interpretation of results (2018)

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