TY - JOUR T1 - R Statistics: survey and review of packages for the estimation of Rasch models AU - Linacre, J.M. KW - r statistics KW - r packages KW - estimation of rasch models KW - large dichotomous data frame KW - PY - 2022/06/24 Y1 - 2022/06/06 VL - 13 N1 - doi: 10.5116/ijme.629d.d88f DO - 10.5116/ijme.629d.d88f M3 - doi: 10.5116/ijme.629d.d88f JO - Int J Med Educ SP - 171 EP - 175 PB - IJME SN - 2042-6372 UR - http://www.ijme.net/archive/13/r-statistics-rasch-models/ L1 - http://www.ijme.net/archive/13/r-statistics-rasch-models.pdf N2 - Abstract: R Statistics is a comprehensive and widely-used suite of packages for statistical operations. From 27 R packages indexed with the word “Rasch”, 11 packages capable of Rasch estimation and analysis are identified and critiqued. A commercial Rasch application is included for comparison. Three R data frames are used. A larger and a smaller 0/1 data frame are analyzed with the Dichotomous Rasch Model. A polytomous 0/1/2 data frame is analyzed with the Partial Credit Model. The R packages can all use the same data frame. They are easy to use and mostly fast, though their documentation is generally skimpy. Every package has obvious shortcomings, but the unique features of each package could make them all useful. For general Rasch estimation and fit analysis of dichotomous data, three packages stand out: eRm, TAM and autoRasch. Two packages stand out for polytomous data: TAM and autoRasch. ER -