Authors@R: c(person("Ivailo", "Partchev", email = "partchev@gmail.com", role = c("aut","cre")),
person("Gunter", "Maris", email = "Gunter.Maris@act.org", role = "aut"),
person("Tamaki", "Hattori", role = "ctb"))

Title: Power Analysis for IRT Models Using the Wald, LR, Score, and
person("Felix", "Zimmer", , "felix.zimmer@mail.de", role = c("aut", "cre"),
person("Rudolf", "Debelak", , "rudolf.debelak@psychologie.uzh.ch", role = c("aut"),
Description: Implementation of analytical and sampling-based power analyses for
the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied
to item response theory (IRT) models that are fitted using marginal maximum
likelihood estimation. The methods are described in our paper
Imports: mirt, Deriv, digest, spatstat.random, ggplot2, methods
Author: Felix Zimmer [aut, cre] (<https://orcid.org/0000-0002-8127-0007>),

Title: Estimation of Tree-Based Item Response Models
Author: Ivailo Partchev and Paul De Boeck
Description: Helper functions and example data sets accompanying De
Boeck, P. and Partchev, I. (2012) IRTrees: Tree-Based Item
Response Models of the GLMM Family, Journal of Statistical

Author: Zhaojun Li, Ivailo Partchev and Paul De Boeck
Description: Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.
Suggests: lme4, mirt, knitr, flextable, reshape2

Description: Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Depends: R(>= 2.11.0), methods, stats, graphics

Title: The Iterative Signature Algorithm
Description: The ISA is a biclustering algorithm that finds modules in
an input matrix. A module or bicluster is a block of the
Suggests: igraph (>= 0.5.2), biclust, lattice