Fitting Survival Regression Models via 'Stan'
Fitting Accelerated Failure Time Models
Fitting Accelerated Hazard Models
Akaike information criterion
anova method for survstan models
Estimated regression coefficients
Confidence intervals for the regression coefficients
Computes the crossing survival times
Generic S3 method cross_time
Fitting Extended Hazard Models
Support Functions for emmeans
Parameters estimates of a survstan model
Extract AIC from a Fitted Model
The Generalized Gamma Distribution (Prentice's alternative parametriza...
Generic S3 method ggresiduals
ggresiduals method for survstan models
The Generalized Gamma Distribution (Stacy's original parametrization)
The Gompertz Distribution
Extract Log-Likelihood from a Fitted Model
Model.matrix method for survstan models
Fitting Proportional Hazards Models
Fitting Proportional Odds Models
Print the summary.survstan output
Rank a collection of survstan models
Objects exported from other packages
residuals method for survstan models
Generic S3 method se
Estimated standard errors
Summary for a survstan object
survfit method for survstan models
The 'survstan' package.
Tidy a survstan object
Variance-covariance matrix
Fitting Yang and Prentice Models
Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.
Useful links