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.
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[aut, cre],
Shaobo Jin