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