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