[MinusLogLik,Grad,Hess,V] = diffloglik(M,Inp,Range,PList,...)
M [ model ] - Model object whose likelihood function will be differentiated.
Inp [ cell | struct ] - Input data from which measurement variables will be taken.
Range [ numeric ] - Date range on which the likelihood function will be evaluated.
PList [ cellstr ] - List of model parameters with respect to which the likelihood function will be differentiated.
MinusLogLik [ numeric ] - Value of minus the likelihood function at the input data.
Grad [ numeric ] - Gradient (or score) vector.
Hess [ numeric ] - Hessian (or information) matrix.
V [ numeric ] - Estimated variance scale factor if the 'relative=' options is true; otherwise v is 1.
'chkSstate=' [ true | false | cell ] - Check steady state in each iteration; works only in non-linear models.
'refresh=' [ true | false ] - Refresh dynamic links for each change in a parameter.
'solve=' [ true | false | cellstr ] - Re-compute solution for each parameter change; you can specify a cell array with options for the solve function.
'sstate=' [ true | false | cell ] - Re-compute steady state in each differentiation step; if the model is non-linear, you can pass in a cell array with options used in the sstate function.
See help on model/filter for other options available.