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