This is used to set various numeric parameters controlling a Cox model fit.
Typically it would only be used in a call to `coxph`

.

```
coxph.control(eps = 1e-09, toler.chol = .Machine$double.eps^0.75,
iter.max = 20, toler.inf = sqrt(eps), outer.max = 10, timefix=TRUE)
```

eps

Iteration continues until the relative change in the log partial likelihood is less than eps. Must be positive.

toler.chol

Tolerance for detection of singularity during a Cholesky decomposition of the variance matrix, i.e., for detecting a redundant predictor variable.

iter.max

Maximum number of iterations to attempt for convergence.

toler.inf

Tolerance criteria for the warning message about a possible infinite coefficient value.

outer.max

For a penalized coxph model, e.g. with pspline terms, there is an outer loop of iteration to determine the penalty parameters; maximum number of iterations for this outer loop.

timefix

Resolve any near ties in the time variables.

a list containing the values of each of the above constants

See the vignette "Roundoff error and tied times" for a more
detailed explanation of the `timefix`

option. In short, when
time intervals are created via subtraction then two time intervals that are
actually identical can appear to be different due to floating point
round off error, which in turn can make `coxph`

and
`survfit`

results dependent
on things such as the order in which operations were done or the
particular computer that they were run on.
Such cases are unfortunatedly not rare in practice.
The `timefix=TRUE`

option adds
logic similar to `all.equal`

to ensure reliable results.
In analysis of simulated data sets, however, where often by defintion there
can be no duplicates, the option will often need to be set to
`FALSE`

to avoid spurious merging of close numeric values.