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