[R-pkgs] package ltm -- version 0.9-0
d.rizopoulos at erasmusmc.nl
Mon Mar 2 19:11:27 CET 2009
I'd like to announce the release of the new version of package 'ltm'
(i.e., ltm_0.9-0 soon available from CRAN) for Item Response Theory
analyses. This package provides a flexible framework for analyzing
dichotomous and polytomous data under various IRT models. Furthermore,
supporting functions for descriptive statistics, goodness-of-fit,
ability estimation and plotting are available.
New features include:
* function gpcm() (along with supporting methods, for the generic
functions, anova, plot, margins, factor.scores, summary, vcov) has been
added for fitting the Generalized Partial Credit Model. The 'constraint'
argument of gpcm() can be used to specify constrained versions of the
model. Options are:
- 'constraint = "rasch"' that fits the partial credit model (i.e.,
discrimination parameter fixed at one)
- 'constraint = "1PL"' that specifies a single discrimination
parameter, equal among items, and
- 'constraint = "gpcm"' that specifies a different discrimination
parameter per item.
* gpcm() also allows for mixed type items, i.e., dichotomous and
polytomous; however, for data sets with only dichotomous items,
functions rasch() and ltm() are more efficient.
* The new function GoF.gpcm() calculates the Pearson chi-squared
statistic for Generalized Partial Credit models. A p-value can be also
produced either using the asymptotic chi-squared distribution or a
parametric Bootstrap approach.
* The new function rmvordlogis() can be used to simulate ordinal data
under the Graded Response and the Generalized Partial Credit models.
* for more details and other new features check the NEWS file that
ships with the package.
More information as well as *.R files illustrating the capabilities of
the package can be found in the R-wiki page of 'ltm' available at:
As always, any kind of feedback (e.g., questions, suggestions,
bug-reports, etc.) is more than welcome.
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
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