| Name : R-irtplay
| |
| Version : 1.6.5
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.8
| Date : 2024-06-14 12:04:33
|
| Group : Development/Libraries/Other
| Source RPM : R-irtplay-1.6.5-lp153.1.8.src.rpm
|
| Size : 2.98 MB
| |
| Packager : (none)
| |
| Summary : Unidimensional Item Response Theory Modeling
|
Description :
Fit unidimensional item response theory (IRT) models to a mixture of
dichotomous and polytomous data, calibrate online item parameters
(i.e., pretest and operational items), estimate examinees\' abilities,
and examine the IRT model-data fit on item-level in different ways as
well as provide useful functions related to unidimensional IRT models.
For the item parameter estimation, the marginal maximum likelihood
estimation via the expectation-maximization (MMLE-EM) algorithm (Bock &
Aitkin (1981) < doi:10.1007/BF02294168>) is used. For the online
calibration, the fixed item parameter calibration method (Kim (2006)
< doi:10.1111/j.1745-3984.2006.00021.x>) and the fixed ability parameter
calibration method (Ban, Hanson, Wang, Yi, & Harris (2011)
< doi:10.1111/j.1745-3984.2001.tb01123.x>) are provided. For the ability
estimation, several popular scoring methods (e.g., MLE, EAP, and MAP)
are implemented. In terms of assessing the IRT model-data fit, one of
distinguished features of this package is that it gives not only
well-known item fit statistics (e.g., chi-square (X2), likelihood ratio
chi-square (G2), infit and oufit statistics, and S-X2 statistic (Ames &
Penfield (2015) < doi:10.1111/emip.12067>)) but also graphical displays
to look at residuals between the observed data and model-based
predictions (Hambleton, Swaminathan, & Rogers (1991,
ISBN:9780803936478)). In addition, there are many useful functions such
as analyzing differential item functioning, computing asymptotic
variance-covariance matrices of item parameter estimates (Li & Lissitz
(2004) < doi:10.1111/j.1745-3984.2004.tb01109.x>), importing item and/or
ability parameters from popular IRT software, running \'flexMIRT\' (Cai,
2017) through R, generating simulated data, computing the conditional
distribution of observed scores using the Lord-Wingersky recursion
formula (Lord & Wingersky (1984) < doi:10.1177/014662168400800409>),
computing the loglikelihood of individual items, computing the
loglikelihood of abilities, computing item and test information
functions, computing item and test characteristic curve functions, and
plotting item and test characteristic curves and item and test
information functions. See Lim and Wells (2022)
< doi:10.18637/jss.v103.i12> for more details.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
