Suggests: boot, latticeExtra, directlabels, shiny, knitr, markdown,

Imports: GPArotation, Matrix, Rcpp, mgcv, vegan, Deriv, splines,

Imports: GPArotation, gridExtra, Matrix, Rcpp, mgcv, vegan, Deriv,

Suggests: testthat, plink, boot, latticeExtra, shiny, knitr, parallel

Imports: GPArotation, Rcpp, sfsmisc, numDeriv
Suggests: testthat, boot, latticeExtra, shiny, knitr, parallel, Rsolnp,

Suggests: testthat, boot, latticeExtra, directlabels, shiny, knitr,
parallel, Rsolnp, alabama, sirt, mirtCAT

Title: Computerized Adaptive Testing with Multidimensional Item
"rphilip.chalmers@gmail.com", role = c("aut", "cre", "cph")))
Description: Provides tools to generate an HTML interface for creating adaptive
and non-adaptive educational and psychological tests using the shiny
package. Suitable for applying unidimensional and multidimensional
computerized adaptive tests using item response theory methodology and for
creating simple questionnaires forms to collect response data directly in R.

Depends: mirt (>= 1.8), shiny (>= 0.11.0), lattice

Depends: mirt (>= 1.14), shiny (>= 0.11.0), lattice

Package also contains tools useful for performing Monte Carlo simulations
for studying the behaviour of computerized adaptive test banks.
Suggests: parallel, SimDesign, knitr, testthat

Imports: lattice, stats, Rcpp, methods, markdown

Suitable for applying unidimensional and multidimensional
computerized adaptive tests (CAT) using item response theory methodology and for
Additionally, optimal test designs (e.g., "shadow testing") are supported
for tests which contain a large number of item selection constraints.
Finally, package contains tools useful for performing Monte Carlo simulations
for studying the behavior of computerized adaptive test banks.
Imports: lattice, stats, Rcpp, methods, markdown, pbapply, lpSolve
Suggests: shinythemes, parallel, SimDesign, knitr
Author: Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>)

Authors@R: c(person("Phil", family="Chalmers", email =

Title: Joint Maximum Likelihood Estimation for High-Dimensional Item
person("Siliang", "Zhang", email = "zhangsiliang123@gmail.com", role = c("aut", "cre")),
person("Yunxiao", "Chen", role = "aut"),
Description: Provides constrained joint maximum likelihood estimation
algorithms for item factor analysis (IFA) based on multidimensional item response theory
models. So far, we provide functions for exploratory and confirmatory IFA based on the
multidimensional two parameter logistic (M2PL) model for binary response data. Comparing
with traditional estimation methods for IFA, the methods implemented in this package scale
better to data with large numbers of respondents, items, and latent factors. The computation
is facilitated by multiprocessing 'OpenMP' API. For more information, please refer to:
1. Chen, Y., Li, X., & Zhang, S. (2018). Joint Maximum Likelihood Estimation for
High-Dimensional Exploratory Item Factor Analysis. Psychometrika, 1-23.
2. Chen, Y., Li, X., & Zhang, S. (2017). Structured Latent Factor Analysis for Large-scale Data:
Identifiability, Estimability, and Their Implications. arXiv preprint <arXiv:1712.08966>.
Imports: Rcpp (>= 0.12.17), stats, GPArotation
Packaged: 2018-12-11 13:28:39 UTC; siliangzhang