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