rpf

Computes local dependence indices for all pairs of items

Compute Expected A Posteriori (EAP) scores

Description of LSAT6 data

Description of LSAT7 data

Compute the S fit statistic for a set of items

Compute the S fit statistic for 1 item

Convert an OpenMx MxModel object into an IFA group

Identify the columns with most missing data

Collapse small sample size categorical frequency counts

Compress a data frame into unique rows and frequencies

Monte-Carlo test for cross-tabulation tables

Expand summary table of patterns and frequencies

Convert factor loadings to response function slopes

Convert factor thresholds to response function intercepts

Produce an item outcome by observed sum-score table

Knox Cube Test dataset

Transform from [0,1] to the reals

Multinomial fit test

Compute the observed sum-score

Omit the given items

Omit items with the most missing data

Order a data.frame by missingness and all columns

Compute the ordinal gamma association statistic

Compute the P value that the observed and expected tables come from the same distribution

Read a flexMIRT PRM file

The base class for 1 dimensional response probability functions.

Unidimensional dichotomous item models (1PL, 2PL, and 3PL).

Calculate item and person Rasch fit statistics

Unidimensional generalized partial credit monotonic polynomial.

The base class for 1 dimensional graded response probability functions.

The unidimensional graded response item model.

Unidimensional graded response monotonic polynomial.

Unidimensional logistic function of a monotonic polynomial.

Calculate cell central moments

Calculate residuals

Calculate standardized residuals

The base class for response probability functions.

Item parameter derivatives

Item derivatives with respect to the location in the latent space

Create a dichotomous response model

Create monotonic polynomial generalized partial credit (GPC-MP) model

Create a graded response model

Create monotonic polynomial graded response (GR-MP) model

Convert an rpf item model name to an ID

Map an item model, item parameters, and person trait score into a information vector

rpf - Response Probability Functions

Create logistic function of a monotonic polynomial (LMP) model

Map an item model, item parameters, and person trait score into a probability vector

Create a multiple-choice response model

The base class for multi-dimensional response probability functions.

Multidimensional dichotomous item models (M1PL, M2PL, and M3PL).

The base class for multi-dimensional graded response probability functions.

The multidimensional graded response item model.

The multiple-choice response item model (both unidimensional and multidimensional models have the same parameterization).

The nominal response item model (both unidimensional and multidimensional models have the same parameterization).

Find the point where an item provides mean maximum information

Find the point where an item provides mean maximum information

Create a similar item specification with the given number of factors

Create a nominal response model

Length of the item parameter vector

Length of the item model vector

The ogive constant

Retrieve a description of the given parameter

Map an item model, item parameters, and person trait score into a probability vector

Rescale item parameters

Generates item parameters

Randomly sample response patterns given a list of items

Liking for Science dataset

Strip data and scores from an IFA group

Compute the sum-score EAP table

Conduct the sum-score EAP distribution test

Tabulate data.frame rows

Convert response function slopes to factor loadings

Convert response function intercepts to factor thresholds

Write a flexMIRT PRM file