classify: Classification Accuracy and Consistency under IRT models

IRT classification uses the probability that candidates of a given ability, will answer correctly questions of a specified difficulty to calculate the probability of their achieving every possible score in a test. Due to the IRT assumption of conditional independence (that is every answer given is assumed to depend only on the latent trait being measured) the probability of candidates achieving these potential scores can be expressed by multiplication of probabilities for item responses for a given ability. Once the true score and the probabilities of achieving all other scores have been determined for a candidate the probability of their score lying in the same category as that of their true score (classification accuracy), or the probability of consistent classification in a category over administrations (classification consistency), can be calculated.

Version: 1.3
Imports: Rcpp (≥ 0.9.10), plyr, ggplot2, lattice, methods, R2jags, reshape2
LinkingTo: Rcpp
Suggests: R2WinBUGS
Published: 2014-08-17
Author: Dr Chris Wheadon and Dr Ian Stockford
Maintainer: Dr Chris Wheadon <chris.wheadon at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: classify citation info
In views: Psychometrics
CRAN checks: classify results


Reference manual: classify.pdf
Package source: classify_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: classify_1.3.tgz
OS X Mavericks binaries: r-oldrel: classify_1.3.tgz
Old sources: classify archive


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