A general implementation of Structural Equation Models
with latent variables (MLE, 2SLS, and composite likelihood
estimators) with both continuous, censored, and ordinal
outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
Mixture latent variable models and non-linear latent variable models
(Holst and Budtz-Joergensen (2019) <doi:10.1093/biostatistics/kxy082>).
The package also provides methods for graph exploration (d-separation,
back-door criterion), simulation of general non-linear latent variable
models, and estimation of influence functions for a broad range of
statistical models.
Version: |
1.6.7 |
Depends: |
R (≥ 3.0) |
Imports: |
grDevices, graphics, methods, numDeriv, stats, survival, SQUAREM, utils |
Suggests: |
KernSmooth, Matrix, Rgraphviz, data.table, ellipse, fields, foreach, geepack, gof (≥ 0.9), graph, igraph (≥ 0.6), lava.tobit (≥ 0.4.7), lme4, mets (≥ 1.1), nlme, optimx, polycor, quantreg, rgl, testthat (≥ 0.11), visNetwork, zoo |
Published: |
2020-03-05 |
Author: |
Klaus K. Holst [aut, cre],
Brice Ozenne [ctb],
Thomas Gerds [ctb] |
Maintainer: |
Klaus K. Holst <klaus at holst.it> |
BugReports: |
https://github.com/kkholst/lava/issues |
License: |
GPL-3 |
URL: |
https://github.com/kkholst/lava |
NeedsCompilation: |
no |
Citation: |
lava citation info |
Materials: |
NEWS |
In views: |
Psychometrics |
CRAN checks: |
lava results |