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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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.