hlt: Higher-Order Item Response Theory

Higher-order latent trait theory (item response theory). We implement the generalized partial credit model with a second-order latent trait structure. Latent regression can be done on the second-order latent trait. For a pre-print of the methods, see, "Latent Regression in Higher-Order Item Response Theory with the R Package hlt" <https://mkleinsa.github.io/doc/hlt_proof_draft_brmic.pdf>.

Version: 1.3.1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.8), RcppDist, RcppProgress, tidyr, ggplot2, truncnorm, foreach, doParallel
LinkingTo: Rcpp, RcppDist, RcppProgress
Published: 2022-08-22
DOI: 10.32614/CRAN.package.hlt
Author: Michael Kleinsasser [aut, cre]
Maintainer: Michael Kleinsasser <mjkleinsa at gmail.com>
BugReports: https://github.com/mkleinsa/hlt/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mkleinsa/hlt
NeedsCompilation: yes
Materials: README
CRAN checks: hlt results


Reference manual: hlt.pdf


Package source: hlt_1.3.1.tar.gz
Windows binaries: r-devel: hlt_1.3.1.zip, r-release: hlt_1.3.1.zip, r-oldrel: hlt_1.3.1.zip
macOS binaries: r-release (arm64): hlt_1.3.1.tgz, r-oldrel (arm64): hlt_1.3.1.tgz, r-release (x86_64): hlt_1.3.1.tgz, r-oldrel (x86_64): hlt_1.3.1.tgz


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