Ozzy

The algorithm would find its place in one of the existing packages (where ''optim'' or ''signal'' sound appropriate) or as a separate package. I plan to prepare two versions of the general algorithm, (temporal name {{codeline|maxent}})

* a version for problems defined by matrix. The function's declaration should be something like this

where {{codeline|y} is the data vector, and {{codeline|D}} is the transformation matrix. {{codeline|sigma}} should be a vector or scalar which describes standard deviation of values of {{codeline|y}}. The optional parameter {{codeline|alpha}} and {{codeline|model}} describe confidence and a priori distribution of {{codeline|x}} (defaults to flat) respectively. The last parameter {{codeline|optset}} would allow to pass additional parameters to function, similar to the ones in {{codeline|optim}} package.

* another version would be defined for a non-linear function. The declaration would very similar

All the parameters have the similar meaning, and the new parameter {{codeline|fun}} is the handle to a function which accepts vector argument, which describes the problem. This time the returned value should obey <math> y \approx f(x)</math>