person("Matthias", "Brueckner", email="matthias.brueckner@posteo.de", role = c("aut", "cre"))

Flexible Parametric Accelerated Hazards Models
Authors@R. c(person("Abdisalam", "Hassan","Muse"
email="abdisalam.h.muse@gmail.com",
role=c("aut", "ctb","cre")),
person("Samuel", "Mwalili",
role=c("ctb")),
person("Oscar", "Ngesa",
role=c("ctb")),
person("Mutua", "Kilai",
role = c("ctb"))
)
Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.

Model selection and multimodel inference based on (Q)AIC(c)
Marc J. Mazerolle <marc.mazerolle@uqat.ca>.  Special thanks to
T. Ergon for the original idea of storing candidate models in a
list.
This package includes functions to create model selection
tables based on Akaike's information criterion (AIC) and the
second-order AIC (AICc), as well as their quasi-likelihood
counterparts (QAIC, QAICc).  Tables are printed with delta AIC
and Akaike weights.  The package also includes functions to
conduct model averaging (multimodel inference) for a given
parameter of interest or predicted values.  Other handy
functions enable the computation of relative variable
importance, evidence ratios, and confidence sets for the best
model. The present version works with lm, glm, and lme object
classes.

This package includes functions to create model selection
tables based on Akaike's information criterion (AIC) and the
second-order AIC (AICc), as well as their quasi-likelihood
counterparts (QAIC, QAICc).  Tables are printed with delta AIC
and Akaike weights.  The package also includes functions to
conduct model averaging (multimodel inference) for a given
parameter of interest or predicted values.  Other handy
functions enable the computation of relative variable
importance, evidence ratios, and confidence sets for the best
model. The present version works with lm, glm, multinom, and
lme object classes.