R Under development (unstable) (2024-01-28 r85838 ucrt) -- "Unsuffered Consequences"
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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(survobj)
> 
> test_check("survobj")
Loading required package: survival
Valid parameters to define a Exponential distribution are: 
lambda: for the canonical parameter of the distribution, or
surv, t: for the surviving proportion (no events) at time t, or
fail, t: for the failure proportion (events) at time t 

Valid parameters to define a Gompertz distribution are: 
scale and shape: for the canonical parameters of the distribution, or
surv, t and shape: for the surviving proportion (no events) at time t and shape, or
fail, t and shape: for the failure proportion (events) at time t and shape, or 

Valid parameters to define a loglogistic distribution are: 
scale and shape: for the canonical parameters of the distribution, or
surv, t and shape: for the surviving proportion (no events) at time t and shape, or
fail, t and shape: for the failure proportion (events) at time t and shape, or
intercept and scale: for values from a survreg regression

Valid parameters to define a lognormal distribution are: 
scale and shape: for the canonical parameters of the distribution, or
surv, t and shape: for the surviving proportion (no events) at time t and shape, or
fail, t and shape: for the failure proportion (events) at time t and shape, or
intercept and scale: for values from a survreg regression

Valid parameters to define a Piecewise Exponential distribution are: 
breaks and hazards: for the exponential hazard until each break, or
surv, breaks and segments : for the surviving proportion (no events) at the last break, or
fail, breaks and segments: for the failure proportion (events) at the last finite break 

Valid parameters to define a Weibull distribution are: 
scale and shape: for the canonical parameters of the distribution, or
surv, t and shape: for the surviving proportion (no events) at time t and shape, or
fail, t and shape: for the failure proportion (events) at time t and shape, or
intercept and scale: for values from a survreg regression

[ FAIL 0 | WARN 0 | SKIP 0 | PASS 151 ]
> 
> proc.time()
   user  system elapsed 
  37.43    1.17   38.59