lamle
0.3.1Maximum Likelihood Estimation of Latent Variable Models
Overview
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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Health
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 89%
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.3.1Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2023-08-25
- Total releases
- 1 / 3 yrs
- License
- GPL (>= 2) OSI
- Download size
- 76 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet