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]

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hlt.pdf |hlt.html✨
hlt/json (API)

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

Peer review:

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

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

On CRAN:

4 exports 0.00 score 45 dependencies 6 scripts 396 downloads

Last updated 2 years agofrom:e7cd918cf4. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 23 2024
R-4.5-win-x86_64NOTEAug 23 2024
R-4.5-linux-x86_64NOTEAug 23 2024
R-4.4-win-x86_64NOTEAug 23 2024
R-4.4-mac-x86_64NOTEAug 23 2024
R-4.4-mac-aarch64NOTEAug 23 2024
R-4.3-win-x86_64OKAug 23 2024
R-4.3-mac-x86_64OKAug 23 2024
R-4.3-mac-aarch64OKAug 23 2024

Exports:get_hlt_starthlthltsimmerge_chains

Dependencies:clicodetoolscolorspacecpp11doParalleldplyrfansifarverforeachgenericsggplot2gluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpurrrR6RColorBrewerRcppRcppArmadilloRcppDistRcppProgressrlangscalesstringistringrtibbletidyrtidyselecttruncnormutf8vctrsviridisLitewithr