Allow the user to set some characteristics of the `nls`

nonlinear least squares algorithm.

```
nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024,
printEval = FALSE, warnOnly = FALSE)
```

maxiter

A positive integer specifying the maximum number of iterations allowed.

tol

A positive numeric value specifying the tolerance level for the relative offset convergence criterion.

minFactor

A positive numeric value specifying the minimum step-size factor allowed on any step in the iteration. The increment is calculated with a Gauss-Newton algorithm and successively halved until the residual sum of squares has been decreased or until the step-size factor has been reduced below this limit.

printEval

a logical specifying whether the number of evaluations (steps in the gradient direction taken each iteration) is printed.

warnOnly

a logical specifying whether `nls()`

should
return instead of signalling an error in the case of termination
before convergence.
Termination before convergence happens upon completion of `maxiter`

iterations, in the case of a singular gradient, and in the case that the
step-size factor is reduced below `minFactor`

.

A `list`

with exactly five components:

Bates, D. M. and Watts, D. G. (1988),
*Nonlinear Regression Analysis and Its Applications*, Wiley.

```
# NOT RUN {
nls.control(minFactor = 1/2048)
# }
```

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