Package: hlt 1.3.1

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>.

Authors:Michael Kleinsasser [aut, cre]

hlt_1.3.1.tar.gz
hlt_1.3.1.zip(r-4.7)hlt_1.3.1.zip(r-4.6)hlt_1.3.1.zip(r-4.5)
hlt_1.3.1.tgz(r-4.6-x86_64)hlt_1.3.1.tgz(r-4.6-arm64)hlt_1.3.1.tgz(r-4.5-x86_64)hlt_1.3.1.tgz(r-4.5-arm64)
hlt_1.3.1.tar.gz(r-4.7-arm64)hlt_1.3.1.tar.gz(r-4.7-x86_64)hlt_1.3.1.tar.gz(r-4.6-arm64)hlt_1.3.1.tar.gz(r-4.6-x86_64)
hlt_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
hlt/json (API)

# Install 'hlt' in R:
install.packages('hlt', repos = c('https://mkleinsa.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mkleinsa/hlt/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

1.70 score 6 scripts 425 downloads 4 exports 38 dependencies

Last updated from:e7cd918cf4. Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE170
linux-devel-x86_64NOTE151
source / vignettesOK234
linux-release-arm64NOTE162
linux-release-x86_64NOTE162
macos-release-arm64NOTE166
macos-release-x86_64NOTE264
macos-oldrel-arm64NOTE203
macos-oldrel-x86_64NOTE423
windows-develNOTE264
windows-releaseNOTE147
windows-oldrelNOTE150
wasm-releaseOK133

Exports:get_hlt_starthlthltsimmerge_chains

Dependencies:clicodetoolscpp11doParalleldplyrfarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglifecyclemagrittrpillarpkgconfigpurrrR6RColorBrewerRcppRcppArmadilloRcppDistRcppProgressrlangS7scalesstringistringrtibbletidyrtidyselecttruncnormutf8vctrsviridisLitewithr