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: 1.8.1 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 (≥ 1.1.35.1), MASS, Matrix (≥ 1.6.3), mets (≥ 1.1), nlme, optimx, polycor, quantreg, rgl, targeted (≥ 0.4), testthat (≥ 0.11), visNetwork Published: 2025-01-12 DOI: 10.32614/CRAN.package.lava 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://kkholst.github.io/lava/ NeedsCompilation: no Citation: lava citation info Materials: README NEWS In views: Psychometrics CRAN checks: lava results Documentation: Downloads: Reverse dependencies: Reverse depends: lavaSearch2, NMMIPW, targeted Reverse imports: BuyseTest, DIRMR, FunctanSNP, LMMstar, mets, pec, polle, prodlim, Publish, riskRegression, SmoothHazard, timereg Linking:Please use the canonical form https://CRAN.R-project.org/package=lava to link to this page.
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