R Under development (unstable) (2026-02-18 r89435 ucrt) -- "Unsuffered Consequences"
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> 
> # This test will fail without try() wrapper in multiStart()
> require(BB)
Loading required package: BB
> 
> test <- function(x, bb0=-3, bb1=5, c0=2, r0=0) { 
+   ((exp(c0-r0)*(bb0+x)*(bb1-x))/((bb0+x+1)*(bb1-x-1))-1)
+   } 
> 
> # uniroot(test,c(-100,100))$root   this will fail
> 
> p0 <- matrix(seq(0, 6, length=10), ncol=1)
> ans   <- multiStart(par=p0, fn=test, method=2, control=list(M=20, NM=FALSE)) 
Parameter set :  1 ... 
  Unsuccessful convergence.
Parameter set :  2 ... 
  Unsuccessful convergence.
Parameter set :  3 ... 
  Unsuccessful convergence.
Parameter set :  4 ... 
Parameter set :  5 ... 
  Successful convergence.
Parameter set :  6 ... 
  Successful convergence.
Parameter set :  7 ... 
Parameter set :  8 ... 
  Successful convergence.
Parameter set :  9 ... 
  Successful convergence.
Parameter set :  10 ... 
  Successful convergence.
Warning messages:
1: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
2: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
3: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
4: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
5: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
6: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
7: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
8: In dfsane(par = par, fn, method = cpars[1], control = list(M = as.numeric(cpars[2]),  :
  Function returns a scalar. Function BBoptim or spg is better.
> ans$par[ans$conv]
[1] 3.069772 3.069772 5.243263 5.243263 5.243263
> 
> 
> proc.time()
   user  system elapsed 
   0.50    0.07    0.56