Package: lamle 0.3.1
Björn Andersson
lamle: Maximum Likelihood Estimation of Latent Variable Models
Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) <doi:10.1080/10705511.2017.1403287>, for item response theory models in Andersson, B., and Xin, T. (2021) <doi:10.3102/1076998620945199>, and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) <doi:10.1016/j.csda.2023.107710>. Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models.
Authors:
lamle_0.3.1.tar.gz
lamle_0.3.1.tar.gz(r-4.5-noble)lamle_0.3.1.tar.gz(r-4.4-noble)
lamle_0.3.1.tgz(r-4.4-emscripten)lamle_0.3.1.tgz(r-4.3-emscripten)
lamle.pdf |lamle.html✨
lamle/json (API)
NEWS
# Install 'lamle' in R: |
install.packages('lamle', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:1ad72034fa. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 17 2024 |
R-4.5-linux-x86_64 | OK | Dec 17 2024 |
Exports:DGPlamlelamle.computelamle.fitlamle.plotlamle.predictlamle.sim
Dependencies:fastGHQuadmvtnormnumDerivRcppRcppArmadillo