Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).
Convert an IRT item model name to an ID
Mental silence dataset
The ogive constant
Create a graded response model and associated hyperparameters.
Map an item model, item parameters, and person trait score into a
probability vector
Length of the item model vector
Map an item model, item parameters, and person trait score into a
information vector
The base class for 1 dimensional response probability functions.
Compute S-Chi-squared fit statistic for a set of items
Create a dichotomous response model and associated hyperparameters.
Unidimensional dichotomous item models (1PL, 2PL, and 3PL).
Create a multiple-choice response model and associated hyperparameters.
Create a nominal response model and associated hyperparameters.
Calculate standardized residuals
Compute Gauss-Hermite quadrature rule
The base class for response probability functions.
Item parameter gradients
Calculate cell moments
The unidimensional generalized partial credit item model.
Length of the item parameter vector
Find the point where an item provides mean maximum information
Liking for Science dataset
The unidimensional graded response item model.
Create a generalized partial credit model and associated hyperparameters.
Map an item model, item parameters, and person trait score into a
probability vector
Compute S-Chi-squared fit statistic for 1 item
Generates item parameters
The nominal response item model (both unidimensional and
multidimensional models have the same parameterization).
The base class for multi-dimensional response probability functions.
The base class for multi-dimensional graded response probability
functions.
The multiple-choice response item model (both unidimensional and
multidimensional models have the same parameterization).
The multidimensional graded response item model.
The base class for 1 dimensional graded response probability functions.
The multidimensional generalized partial credit item model.
Calculate item and person fit statistics
Find the point where an item provides mean maximum information
Randomly sample response patterns given a list of items
Calculate residuals
Log likelihood of the item model parameters given the Bayesian prior
rpf - Response Probability Functions