lamle-package {lamle}

Maximum Likelihood Estimation of Latent Variable Models

Description

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.

Details

The DESCRIPTION file:

Package:	lamle
Encoding:	UTF-8
Type:	Package
Title:	Maximum Likelihood Estimation of Latent Variable Models
Version:	0.3.1
Date:	2023-08-24
Authors@R:	c(person(given = "Björn", family = "Andersson", role = c("aut", "cre"), email = "[e-mail]", comment = c(ORCID = "0000-0002-9007-2440")), person(given = "Shaobo", family = "Jin", role = c("aut"), email = "[e-mail]", comment = c(ORCID = "0000-0001-6538-3477")), person(given = "Maoxin", family = "Zhang", role = c("ctb"), email = "[e-mail]"))
Description:	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.
License:	GPL (>= 2)
Imports:	Rcpp (>= 1.0.1), mvtnorm, numDeriv, stats, fastGHQuad, methods
LinkingTo:	Rcpp, RcppArmadillo
Author:	Björn Andersson [aut, cre] (<https://orcid.org/0000-0002-9007-2440>), Shaobo Jin [aut] (<https://orcid.org/0000-0001-6538-3477>), Maoxin Zhang [ctb]
Maintainer:	Björn Andersson <[e-mail]>
Archs:	x64

Index of help topics:

DGP                     Generation of Observed Data From a Generalized
                        Linear Latent Variable Model
lamle                   Estimation of Latent Variable Models with the
                        Laplace Approximation or Adaptive Gauss-Hermite
                        Quadrature
lamle-package           Maximum Likelihood Estimation of Latent
                        Variable Models
lamle.compute           Compute Output from an Estimated Latent
                        Variable Model
lamle.fit               Model Fit Statistics for an Estimated Latent
                        Variable Model
lamle.plot              Plot Output from an Estimated Latent Variable
                        Model
lamle.predict           Compute Latent Variable Estimates from an
                        Estimated Latent Variable Model
lamle.sim               Generate Simulated Data from an Estimated
                        Latent Variable Model
lamleout-class          Class "lamleout"

Author(s)

Björn Andersson, Shaobo Jin and Maoxin Zhang.

Maintainer: Björn Andersson <[e-mail]>

References

Andersson, B., Jin, S., and Zhang, M. (2023). Fast estimation of multiple group generalized linear latent variable models for categorical observed variables. Computational Statistics and Data Analysis, 182, 1-12. <doi:10.1016/j.csda.2023.107710>

Andersson, B., and Xin, T. (2021). Estimation of latent regression item response theory models using a second-order Laplace approximation. Journal of Educational and Behavioral Statistics, 46(2), 244-265. <doi:10.3102/1076998620945199>

Huber, P., Ronchetti, E., and Victoria-Feser, M.P. (2004). Estimation of generalized linear latent variable models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), 893-908. <doi:10.1111/j.1467-9868.2004.05627.x>

Jin, S., Noh, M., and Lee, Y. (2018). H-Likelihood Approach to Factor Analysis for Ordinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 530-540. <doi:10.1080/10705511.2017.1403287>

Shun, Z., and McCullagh, P. (1995). Laplace approximation of high dimensional integrals. Journal of the Royal Statistical Society: Series B (Methodological), 57(4), 749-760. <doi:10.1111/j.2517-6161.1995.tb02060.x>