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Estimating likelihood functions entails a two-step process. First, one declares the log-likelihood function. then one optimizes the log-likelihood functions. that's fine. writing the log-likelihood functions in R, we ask for -1*l (where l represents the log - likelihood function) because the optim command in R minimizes a function by default. minimization of -l is the same as maximization of l, which is what we want. Now, the observed Fisher Information Matrix is equal to (-H)^-1. the reason that we do not have to mulitply the hassian by -1 is that all of the evaluation has been done in terms of -1 times the log-likelihood. this means that the hessian that is produced by optim is already multiplied by -1