Package: irtpwr 1.0.3.9000

irtpwr: Power Analysis for IRT Models Using the Wald, LR, Score, and Gradient Statistics

Implementation of analytical and sampling-based power analyses for the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied to item response theory (IRT) models that are fitted using marginal maximum likelihood estimation. The methods are described in our paper (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).

Authors:Felix Zimmer [aut, cre], Rudolf Debelak [aut], Jan Radek [ctb]

irtpwr_1.0.3.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
irtpwr/json (API)

# Install 'irtpwr' in R:
install.packages('irtpwr', repos = c('https://flxzimmer.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/flxzimmer/irtpwr/issues

On CRAN:

Conda:

4.60 score 2 stars 9 scripts 151 downloads 5 exports 92 dependencies

Last updated from:faa97fd860. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK176
source / vignettesOK309
linux-release-x86_64OK148
macos-release-arm64OK119
macos-oldrel-arm64OK113
windows-develOK73
windows-releaseOK81
windows-oldrelOK86
wasm-releaseOK124

Exports:calc.timeirtpwrload.functionspars.longsetup.hypothesis

Dependencies:abindaudiobeeprbriocallrclassclicliprclustercodetoolscpp11crayondcurverdeldirDerivdescdiffobjdigestdplyre1071evaluatefarverfsfuturefuture.applygenericsggplot2globalsglueGPArotationgridExtragtableisobandjsonlitelabelinglatticelifecyclelistenvmagrittrMASSMatrixmgcvmiraimirtnanonextnlmeparallellypbapplypermutepillarpkgbuildpkgconfigpkgloadpolyclippraiseprocessxprogressrproxypsqs2R.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppArmadilloRcppParallelrlangrprojrootS7scalessessioninfoSimDesignspatstat.dataspatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilssplines2stringfishtensortestthattibbletidyselectutf8vctrsveganviridisLitewaldowithr

Power Analysis for the Wald, LR, Score, and Gradient Tests using irtpwr
1. Introduction | 2. General workflow for irtpwr | 3. Rasch against 2PL model | 3.1. A priori power analysis | 3.2. A posteriori power analysis | 4. DIF analysis in 2PL model | 4.1. A priori power analysis | 4.2. A posteriori power analysis
Rendered frompower_analysis.Rmdusingknitr::rmarkdown

Last update: 2023-09-29
Started: 2023-09-07

Hypothesis Templates
h_1PLvs2PL | h_DIF2PL | h_PCMvsGPCM | h_2PL_basic | h_3PL_basic | h_multi_basic | h_multi_basic2
Rendered fromhypothesis_templates.Rmdusingknitr::rmarkdown

Last update: 2023-09-07
Started: 2022-11-02

Adding Hypotheses
Walkthrough | Function to set up the restricted model | Function to set up the unrestricted model | Function to maximize the likelihood of the restricted parameters | Putting it together | Testing the new hypothesis
Rendered fromadding_hypotheses.Rmdusingknitr::rmarkdown

Last update: 2022-11-09
Started: 2021-03-05

Demo
Introduction | Toy Example | Main Functions and Arguments | setup.hypothesis | irtpwr Function | Further Examples | 3PL Model | Multidimensional Model
Rendered fromdemo.Rmdusingknitr::rmarkdown

Last update: 2022-11-07
Started: 2020-11-02