Computes local dependence indices for all pairs of items
Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).
Compute the P value that the observed and expected tables come from the same distribution
Map an item model, item parameters, and person trait score into a
information vector
Order a data.frame by missingness and all columns
The base class for multi-dimensional graded response probability
functions.
Compute EAP scores
Calculate item and person Rasch fit statistics
Compute the S fit statistic for 1 item
Convert an rpf item model name to an ID
Calculate standardized residuals
The ogive constant
Length of the item parameter vector
Compute the ordinal gamma association statistic
Find the point where an item provides mean maximum information
The nominal response item model (both unidimensional and
multidimensional models have the same parameterization).
Find the point where an item provides mean maximum information
Item parameter derivatives
Expand summary table of patterns and frequencies
Item derivatives with respect to the location in the latent space
Map an item model, item parameters, and person trait score into a
probability vector
Compute the sum-score EAP table
Multinomial fit test
Compress a data frame into unique rows and frequencies
Randomly sample response patterns given a list of items
Retrieve a description of the given parameter
The base class for response probability functions.
Tabulate data.frame rows
Create a graded response model
Read a flexMIRT PRM file
Create a multiple-choice response model
The multidimensional graded response item model.
Monte-Carlo test for cross-tabulation tables
Create a dichotomous response model
Generates item parameters
The base class for multi-dimensional response probability functions.
Produce an item outcome by observed sum-score table
Transform from [0,1] to the reals
Calculate residuals
Compute the S fit statistic for a set of items
Compute the observed sum-score
Liking for Science dataset
The unidimensional graded response item model.
rpf - Response Probability Functions
The base class for 1 dimensional graded response probability functions.
Rescale item parameters
The multiple-choice response item model (both unidimensional and
multidimensional models have the same parameterization).
Create a nominal response model
Write a flexMIRT PRM file
Length of the item model vector
Create a similar item specification with the given number of factors
Map an item model, item parameters, and person trait score into a
probability vector
Knox Cube Test dataset
Unidimensional dichotomous item models (1PL, 2PL, and 3PL).
Calculate cell central moments
The base class for 1 dimensional response probability functions.