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Title: rpf

Why use RPF?

The idea behind RPF is modularity. Most item factor analysis software is not modular. Modularity facilitates more contributors and cross pollination between projects.

Installation

To get the current released version from CRAN:

install.packages("rpf")

Developer notes

There are a number of useful scripts in the tools subdir:

install -- Installs the package as quickly as possible. Skips building the vignettes and documentation.
build -- Builds a source tarball
check -- Builds a source tarball and checks it
rox -- Re-generates the documentation.
test -- Runs the test suite using the uninstalled tests against the installed package.
autodep -- Recalculates the header file dependences

If you're working on the C++ code, you probably want to adjust the settings in src/Makevars.

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Version

Install

install.packages('rpf')

Monthly Downloads

18,686

Version

1.0.14

License

GPL (>= 3)

Issues

Pull Requests

Stars

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Repository

https://github.com/jpritikin/rpf

Maintainer

Joshua Pritikin

Last Published

August 21st, 2023

Functions in rpf (1.0.14)

observedSumScore

Compute the observed sum-score

Monte-Carlo test for cross-tabulation tables

Transform from [0,1] to the reals

expandDataFrame

Expand summary table of patterns and frequencies

Multinomial fit test

fromFactorLoading

Convert factor loadings to response function slopes

Omit the given items

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Knox Cube Test dataset

fromFactorThreshold

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

Class rpf.1dim.gpcmp

Unidimensional generalized partial credit monotonic polynomial.

omitMostMissing

Omit items with the most missing data

orderCompletely

Order a data.frame by missingness and all columns

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

Class rpf.1dim.lmp

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

Class rpf.1dim.grm

The unidimensional graded response item model.

Class rpf.1dim.grmp

Unidimensional graded response monotonic polynomial.

Class rpf.1dim.drm

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

ptw2011.gof.test

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

rpf.1dim.moment

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

Class rpf.mdim.graded

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

Class rpf.mdim.drm

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

Class rpf.mdim.nrm

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

Class rpf.mdim.grm

The multidimensional graded response item model.

Class rpf.mdim.mcm

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

rpf.1dim.residual

Calculate residuals

rpf.1dim.stdresidual

Calculate standardized residuals

Randomly sample response patterns given a list of items

Liking for Science dataset

toFactorLoading

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

toFactorThreshold

Convert response function intercepts to factor thresholds

Strip data and scores from an IFA group

Compute the sum-score EAP table

An introduction

rpf - Response Probability Functions

Create a similar item specification with the given number of factors

sumScoreEAPTest

Conduct the sum-score EAP distribution test

Tabulate data.frame rows

Convert an OpenMx MxModel object into an IFA group

ChenThissen1997

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

collapseCategoricalCells

Collapse small sample size categorical frequency counts

compressDataFrame

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