lava: Latent Variable Models

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 (2020) <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: 1.8.0
Depends: R (≥ 3.0)
Imports: cli, future.apply, graphics, grDevices, methods, numDeriv, progressr, stats, survival, SQUAREM, utils
Suggests: KernSmooth, Rgraphviz, data.table, ellipse, fields, geepack, graph, knitr, rmarkdown, igraph (≥ 0.6), lavaSearch2, lme4 (≥, MASS, Matrix (≥ 1.6.3), mets (≥ 1.1), nlme, optimx, polycor, quantreg, rgl, targeted (≥ 0.4), testthat (≥ 0.11), visNetwork
Published: 2024-03-05
DOI: 10.32614/CRAN.package.lava
Author: Klaus K. Holst [aut, cre], Brice Ozenne [ctb], Thomas Gerds [ctb]
Maintainer: Klaus K. Holst <klaus at>
License: GPL-3
NeedsCompilation: no
Citation: lava citation info
Materials: README NEWS
In views: Psychometrics
CRAN checks: lava results


Reference manual: lava.pdf
Vignettes: Estimating partial correlations with lava
The Art of Influence
Non-linear latent variable models and error-in-variable models


Package source: lava_1.8.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lava_1.8.0.tgz, r-oldrel (arm64): lava_1.8.0.tgz, r-release (x86_64): lava_1.8.0.tgz, r-oldrel (x86_64): lava_1.8.0.tgz
Old sources: lava archive

Reverse dependencies:

Reverse depends: lavaSearch2, NMMIPW, targeted
Reverse imports: BuyseTest, FunctanSNP, LMMstar, mets, pec, polle, prodlim, Publish, riskRegression, SmoothHazard, timereg


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