[R] MLE optimization

[jckval]

Folks,

I'm kind of newbie in R, but with some background in Matlab and VBA
programming. Last month I was implementing a Maximum Likelihood Estimation
in Matlab, but the algorithms didn't converge. So my academic advisor
suggested using R. My problem is: estimate a mean reverting jump diffusion
parameters. I've succeeded in deriving the likelihood function (which looks
like a gaussian mixture) and it is implemented in R. My main doubts are
related to the inputs and outputs that this function should generate, for
instance, in Matlab this function should get the parameters as input and
output the likelihood using the sample data (imported within the function).
In order to make R optimizers to work I, apparently, should write a function
that uses the parameters and the sample data as input and outputs the
likelihood. Is it correct?
Could someone reply with an example code which examplifies the type of
function I should write and the syntax to optimize?
Alternatively, could anyone suggest a good MLE tutorial and package? 

Thankfully,

JC
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