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.
- Version0.3.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release08/25/2023
Documentation
Team
Björn Andersson
Shaobo Jin
Maoxin Zhang
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- Imports4 packages
- Linking To2 packages