Package: eRm 1.0-9
eRm: Extended Rasch Modeling
Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.
Authors:
eRm_1.0-9.tar.gz
eRm_1.0-9.tar.gz(r-4.5-noble)eRm_1.0-9.tar.gz(r-4.4-noble)
eRm.pdf |eRm.html✨
eRm/json (API)
NEWS
# Install 'eRm' in R: |
install.packages('eRm', repos = 'https://cloud.r-project.org') |
- llraDat1 - An Artificial LLRA Data Set
- llraDat2 - An Artificial LLRA Data Set
- llradat3 - An Artificial LLRA Data Set
- lltmdat1 - Data for Computing Extended Rasch Models
- lltmdat2 - Data for Computing Extended Rasch Models
- lpcmdat - Data for Computing Extended Rasch Models
- lrsmdat - Data for Computing Extended Rasch Models
- pcmdat - Data for Computing Extended Rasch Models
- pcmdat2 - Data for Computing Extended Rasch Models
- raschdat1 - Data for Computing Extended Rasch Models
- raschdat1_RM_fitted - Data for Computing Extended Rasch Models
- raschdat1_RM_lrres2 - Data for Computing Extended Rasch Models
- raschdat1_RM_plotDIF - Data for Computing Extended Rasch Models
- raschdat2 - Data for Computing Extended Rasch Models
- raschdat3 - Data for Computing Extended Rasch Models
- raschdat4 - Data for Computing Extended Rasch Models
- rsmdat - Data for Computing Extended Rasch Models
- xmpl - Example Data
- xmplbig - Example Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 days agofrom:a760ef01f4. Checks:3 OK. Indexed: no.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 25 2025 |
R-4.5-linux-x86_64 | OK | Mar 25 2025 |
R-4.4-linux-x86_64 | OK | Mar 25 2025 |
Exports:build_Wcoef.pparcollapse_WgofIRTi_infoICitem_infoitemfitLLRAllra.datprepLLTMLPCMLRSMLRtestMLoefNPtestPCMperson.parameterpersonfitPersonMisfitphi.rangeplotDIFplotGOFplotGRplotICCplotINFOplotjointICCplotPImapplotPWmapplotTRpmatRMrsamplerrsctrlrsextrmatrsextrobjRSMrstatsSepRelsim.2plsim.locdepsim.raschsim.xdimstepwiseIttest_infothresholdsWaldtest
Dependencies:colorspaceGPArotationlatticeMASSMatrixmnormtnlmepsych
Citation
The original JSS article: Article about CML Estimation in eRm: Article about LLRAs in eRm: Book Chapter about LLRAs: Article about the performance of Quasi-Exact Tests in eRm: Article about the Performance of the nonparametric Q3 Tests in eRm:
Mair P, Hatzinger R (2007). “Extended Rasch modeling: The eRm package for the application of IRT models in R.” Journal of Statistical Software, 20. doi:10.18637/jss.v020.i09.
Mair P, Hatzinger R (2007). “CML based estimation of extended Rasch models with the eRm package in R.” Psychology Science, 49. doi:10.18637/jss.v020.i09.
Hatzinger R, Rusch T (2009). “IRT models with relaxed assumptions in eRm: A manual-like instruction.” Psychology Science Quarterly, 51.
Rusch T, Maier M, Hatzinger R (2013). “Linear logistic models with relaxed assumptions in R.” In Lausen B, van den Poel D, Ultsch A (eds.), Algorithms from and for Nature and Life, series Studies in Classification, Data Analysis, and Knowledge Organization. doi:10.1007/978-3-319-00035-0_34.
Koller I, Maier M, Hatzinger R (2015). “An empirical power analysis of quasi-exact tests for the Rasch model: Measurement invariance in small samples.” Methodology, 11. doi:10.1027/1614-2241/a000090.
Debelak R, Koller I (2019). “Testing the local independence assumption of the Rasch model with Q3-based nonparametric model tests.” Applied Psychological Measurement, 44. doi:10.1177/0146621619835501.
Corresponding BibTeX entries:
@Article{, title = {Extended Rasch modeling: The eRm package for the application of IRT models in R}, author = {Patrick Mair and Reinhold Hatzinger}, journal = {Journal of Statistical Software}, year = {2007}, page = {1--20}, volume = {20}, issue = {9}, doi = {10.18637/jss.v020.i09}, }
@Article{, title = {CML based estimation of extended Rasch models with the eRm package in R}, author = {Patrick Mair and Reinhold Hatzinger}, journal = {Psychology Science}, year = {2007}, page = {26--43}, volume = {49}, issue = {1}, doi = {10.18637/jss.v020.i09}, }
@Article{, title = {IRT models with relaxed assumptions in eRm: A manual-like instruction}, author = {Reinhold Hatzinger and Thomas Rusch}, journal = {Psychology Science Quarterly}, year = {2009}, page = {87--120}, volume = {51}, issue = {1}, }
@InProceedings{, title = {Linear logistic models with relaxed assumptions in R}, author = {Thomas Rusch and Marco Maier and Reinhold Hatzinger}, booktitle = {Algorithms from and for Nature and Life}, editor = {Berthold Lausen and Dirk {van den Poel} and Alfred Ultsch}, series = {Studies in Classification, Data Analysis, and Knowledge Organization}, year = {2013}, page = {337--347}, address = {New York}, publisher = {Springer}, doi = {10.1007/978-3-319-00035-0_34}, }
@Article{, title = {An empirical power analysis of quasi-exact tests for the Rasch model: Measurement invariance in small samples}, author = {Ingrid Koller and Marco Maier and Reinhold Hatzinger}, journal = {Methodology}, year = {2015}, page = {45--54}, volume = {11}, issue = {2}, doi = {10.1027/1614-2241/a000090}, }
@Article{, title = {Testing the local independence assumption of the Rasch model with Q3-based nonparametric model tests}, author = {Rudolf Debelak and Ingrid Koller}, journal = {Applied Psychological Measurement}, year = {2019}, page = {103--117}, volume = {44}, issue = {2}, doi = {10.1177/0146621619835501}, }