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