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lamle: Maximum Likelihood Estimation of Latent Variable Models

Approximate marginal maximum likelihood estimation of multidimensionallatent 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 includethe generalizedpartial 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 multiplegroup models.

Version:	0.3.1
Imports:	Rcpp (≥ 1.0.1),mvtnorm,numDeriv, stats,fastGHQuad, methods
LinkingTo:	Rcpp,RcppArmadillo
Published:	2023-08-25
DOI:	10.32614/CRAN.package.lamle
Author:	Björn Andersson [aut, cre], Shaobo Jin [aut], Maoxin Zhang [ctb]
Maintainer:	Björn Andersson <bjoern.h.andersson at gmail.com>
License:	GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	lamle results

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