Maximum Likelihood Estimation of Latent Variable Models
Generation of Observed Data From a Generalized Linear Latent Variable ...
tools:::Rd_package_title("lamle")
Compute Output from an Estimated Latent Variable Model
Model Fit Statistics for an Estimated Latent Variable Model
Plot Output from an Estimated Latent Variable Model
Compute Latent Variable Estimates from an Estimated Latent Variable Mo...
Estimation of Latent Variable Models with the Laplace Approximation or...
Generate Simulated Data from an Estimated Latent Variable Model
Class "lamleout"
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.