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