The function sets controls for the `gamlssMX`

function.

```
MX.control(cc = 1e-04, n.cyc = 200, trace = FALSE,
seed = NULL, plot = TRUE, sample = NULL, ...)
```

Returns a list

- cc
convergent criterion for the EM

- n.cyc
number of cycles for EM

- trace
whether to print the EM iterations

- seed
a number for setting the seeds for starting values

- plot
whether to plot the sequence of global deviance up to convergence

- sample
how large the sample to be in the starting values

- ...
for extra arguments

Mikis Stasinopoulos and Bob Rigby

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
*Appl. Statist.*, **54**, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
*Distributions for modeling location, scale, and shape: Using GAMLSS in R*, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
*Journal of Statistical Software*, Vol. **23**, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
*Flexible Regression and Smoothing: Using GAMLSS in R*, Chapman and Hall/CRC.

Stasinopoulos M.D., Kneib T, Klein N, Mayr A, Heller GZ. (2024) Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications. Cambridge University Press.

(see also https://www.gamlss.com/).

`gamlss`

, `gamlssMX`

, `gamlssMXfits`