R package flexsurv: Flexible Parametric Survival and Multi-State Models. Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models.

References in zbMATH (referenced in 20 articles , 1 standard article )

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  1. Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
  2. de Freitas Costa, Eduardo; Schneider, Silvana; Carlotto, Giulia Bagatini; Cabalheiro, Tainá; de Oliveira Júnior, Mauro Ribeiro: Zero-inflated-censored Weibull and gamma regression models to estimate wild boar population dispersal distance (2021)
  3. Hadrien Charvat, Aurelien Belot: mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect (2021) not zbMATH
  4. Machado, Robson J. M.; van den Hout, Ardo; Marra, Giampiero: Penalised maximum likelihood estimation in multi-state models for interval-censored data (2021)
  5. Samuel L. Brilleman, Rory Wolfe, Margarita Moreno-Betancur, Michael J. Crowther: Simulating Survival Data Using the simsurv R Package (2021) not zbMATH
  6. Fuino, Michel; Wagner, Joël: Duration of long-term care: socio-economic factors, type of care interactions and evolution (2020)
  7. Gianluca Baio: survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling (2020) not zbMATH
  8. Magalhães, Tiago M.; Gallardo, Diego I.: Bartlett and Bartlett-type corrections for censored data from a Weibull distribution (2020)
  9. Renato Valladares Panaro: spsurv: An R package for semi-parametric survival analysis (2020) arXiv
  10. Torsten Hothorn: Most Likely Transformations: The mlt Package (2020) not zbMATH
  11. Sarabia, José María; Guillen, Montserrat; Chuliá, Helena; Prieto, Faustino: Tail risk measures using flexible parametric distributions (2019)
  12. Tarak Kharrat; Georgi Boshnakov; Ian McHale; Rose Baker: Flexible Regression Models for Count Data Based on Renewal Processes: The Countr Package (2019) not zbMATH
  13. van den Hout, Ardo; Tan, Wenhui: Flexible parametric multistate modelling of employment history (2019)
  14. Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee: SemiCompRisks: An R Package for Independent and Cluster-Correlated Analyses of Semi-Competing Risks Data (2018) arXiv
  15. Sharples, Linda D.: The role of statistics in the era of big data: electronic health records for healthcare research (2018)
  16. Snider, Joseph: Indistinguishable synapses lead to sparse networks (2018)
  17. Antoine Filipovic-Pierucci, Kevin Zarca, Isabelle Durand-Zaleski: Markov Models for Health Economic Evaluations: The R Package heemod (2017) arXiv
  18. Clifford Anderson-Bergman: icenReg: Regression Models for Interval Censored Data in R (2017) not zbMATH
  19. Vanegas, Luis Hernando; Paula, Gilberto A.: Log-symmetric regression models under the presence of non-informative left- or right-censored observations (2017)
  20. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016) not zbMATH