In item response theory models , tests can be modelled through different models, in IRTpp each test calibrated parameters are in a simple data structure that is passed around the IRTpp functions.
In UIRT models, typically Rasch , 2PL and 3PL, in IRTpp the item parameters are usually these :
IRTpp functions must accept these parameters and transform them accordingly in the functions.
The UIRT model types accepted by IRTpp are :
Therefore functions are required to :
model.transform // Transform a model parameters ? model.check // Check if a model is valid or not. model.extract // Extract a named parameter from a parameter list. as.model // Makes a irtpp model from whatever you try to throw at it.
$a $b $c $model $dims
In spite of generalism , the probability function is completely flexible for all models. To calculate a probability we need the model, the parameters and the abilities.
irtpp : estimate item parameters latent.traits : estimate individual parameters
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