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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

23,765

Version

1.0.7

License

GPL (>= 3)

Issues

Pull Requests

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Repository

https://github.com/jpritikin/rpf

Maintainer

Joshua Pritikin

Last Published

April 29th, 2021

Functions in rpf (1.0.7)

itemOutcomeBySumScore

Produce an item outcome by observed sum-score table

Convert an OpenMx MxModel object into an IFA group

Identify the columns with most missing data

The base class for response probability functions.

Calculate item and person Rasch fit statistics

Monte-Carlo test for cross-tabulation tables

Item parameter derivatives

Create logistic function of a monotonic polynomial (LMP) model

collapseCategoricalCells

Collapse small sample size categorical frequency counts

observedSumScore

Compute the observed sum-score

compressDataFrame

Compress a data frame into unique rows and frequencies

expandDataFrame

Expand summary table of patterns and frequencies

ChenThissen1997

Computes local dependence indices for all pairs of items

Compute Expected A Posteriori (EAP) scores

fromFactorLoading

Convert factor loadings to response function slopes

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

fromFactorThreshold

Convert factor thresholds to response function intercepts

Omit the given items

Class rpf.1dim.graded

The base class for 1 dimensional graded response probability functions.

Create a dichotomous response model

Compute the S fit statistic for 1 item

Multinomial fit test

Transform from [0,1] to the reals

Compute the S fit statistic for a set of items

Class rpf.1dim.gpcmp

Unidimensional generalized partial credit monotonic polynomial.

Item derivatives with respect to the location in the latent space

Retrieve a description of the given parameter

omitMostMissing

Omit items with the most missing data

The base class for 1 dimensional response probability functions.

Class rpf.1dim.grmp

Unidimensional graded response monotonic polynomial.

Class rpf.1dim.grm

The unidimensional graded response item model.

Read a flexMIRT PRM file

Compute the ordinal gamma association statistic

rpf.1dim.residual

Calculate residuals

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

orderCompletely

Order a data.frame by missingness and all columns

ptw2011.gof.test

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

An introduction

rpf - Response Probability Functions

rpf.1dim.moment

Calculate cell central moments

Create a graded response model

Class rpf.1dim.lmp

Unidimensional logistic function of a monotonic polynomial.

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

Randomly sample response patterns given a list of items

Class rpf.mdim.graded

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

Create a multiple-choice response model

Class rpf.mdim.drm

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

The base class for multi-dimensional response probability functions.

Find the point where an item provides mean maximum information

Class rpf.mdim.grm

The multidimensional graded response item model.

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

Write a flexMIRT PRM file

Create monotonic polynomial graded response (GR-MP) model

rpf.1dim.stdresidual

Calculate standardized residuals

Convert an rpf item model name to an ID

Rescale item parameters

Generates item parameters

Class rpf.mdim.mcm

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

Length of the item model vector

Create a similar item specification with the given number of factors

The ogive constant

Find the point where an item provides mean maximum information

Tabulate data.frame rows

Class rpf.mdim.nrm

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

sumScoreEAPTest

Conduct the sum-score EAP distribution test

Liking for Science dataset

toFactorThreshold

Convert response function intercepts to factor thresholds

toFactorLoading

Convert response function slopes to factor loadings

Create a nominal response model

Length of the item parameter vector

Compute the sum-score EAP table

Strip data and scores from an IFA group

Description of LSAT6 data

Description of LSAT7 data

Class rpf.1dim.drm

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

Knox Cube Test dataset