Package: RM.weights 2.0

RM.weights: Weighted Rasch Modeling and Extensions using Conditional Maximum Likelihood

Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.

Authors:Carlo Cafiero, Sara Viviani, Mark Nord

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RM.weights.pdf |RM.weights.html✨
RM.weights/json (API)

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

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.38 score 1 stars 24 scripts 238 downloads 8 exports 66 dependencies

Last updated 7 years agofrom:ec3742aef8. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 15 2024
R-4.5-winOKOct 15 2024
R-4.5-linuxOKOct 15 2024
R-4.4-winOKOct 15 2024
R-4.4-macOKOct 15 2024
R-4.3-winOKOct 15 2024
R-4.3-macOKOct 15 2024

Exports:equating.funEWaldtestICC.funPC.wprob.assignRM.wRT.threstab.weight

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigpsychotoolsR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml