Write a flexMIRT PRM file
Map an item model, item parameters, and person trait score into a
probability vector
Produce an item outcome by observed sum-score table
Retrieve a description of the given parameter
Tabulate data.frame rows
Length of the item model vector
Liking for Science dataset
Rescale item parameters
The ogive constant
The base class for 1 dimensional graded response probability functions.
Order a data.frame by missingness and all columns
Randomly sample response patterns given a list of items
Compress a data frame into unique rows and frequencies
Item parameter derivatives
Map an item model, item parameters, and person trait score into a
probability vector
Expand summary table of patterns and frequencies
Create a similar item specification with the given number of factors
Find the point where an item provides mean maximum information
The multiple-choice response item model (both unidimensional and
multidimensional models have the same parameterization).
Omit the given items
Computes local dependence indices for all pairs of items
The base class for multi-dimensional graded response probability
functions.
Compute the sum-score EAP table
Map an item model, item parameters, and person trait score into a
information vector
Compute the P value that the observed and expected tables come from the same distribution
Length of the item parameter vector
Create a graded response model
Compute the ordinal gamma association statistic
Convert an rpf item model name to an ID
Compute the S fit statistic for 1 item
Create a dichotomous response model
Identify the columns with most missing data
Omit items with the most missing data
The base class for 1 dimensional response probability functions.
Calculate cell central moments
Compute the S fit statistic for a set of items
Calculate residuals
Multinomial fit test
The unidimensional graded response item model.
Compute the observed sum-score
Generates item parameters
Transform from [0,1] to the reals
Description of LSAT6 data
Unidimensional dichotomous item models (1PL, 2PL, and 3PL).
Calculate item and person Rasch fit statistics
Conduct the sum-score EAP distribution test
Create a nominal response model
Knox Cube Test dataset
Convert an OpenMx MxModel object into an IFA group
Calculate standardized residuals
The base class for multi-dimensional response probability functions.
The multidimensional graded response item model.
Description of LSAT7 data
The nominal response item model (both unidimensional and
multidimensional models have the same parameterization).
Read a flexMIRT PRM file
Compute EAP scores
The base class for response probability functions.
Item derivatives with respect to the location in the latent space
Find the point where an item provides mean maximum information
rpf - Response Probability Functions
Monte-Carlo test for cross-tabulation tables
Create a multiple-choice response model
Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).
Strip data and scores from an IFA group