mets (>= 0.2.7), graph, Rgraphviz, igraph (>= 0.6), Matrix,
KernSmooth, lme4, geepack, gof (>= 0.9), foreach, rgl,
survival, testthat, ascii

Estimation and simulation of latent variable models.
mets (>= 1.1), graph, Rgraphviz, igraph (>= 0.6), Matrix,
KernSmooth, lme4, geepack, gof (>= 0.9), foreach, rgl, fields,
survival, testthat, ascii

numDeriv, methods, graphics, grDevices, utils, stats
mets (>= 1.1), graph, data.table, Rgraphviz, igraph (>= 0.6),
Matrix, KernSmooth, lme4, geepack, gof (>= 0.9), foreach,
quantreg, rgl, fields, survival, testthat (>= 0.11), ascii, zoo

mets (>= 1.1), graph, optimx, data.table, Rgraphviz, igraph
(>= 0.6), Matrix, KernSmooth, lme4, geepack, gof (>= 0.9),
foreach, quantreg, rgl, fields, survival, testthat (>= 0.11),
ascii, zoo

numDeriv, methods, graphics, grDevices, utils, survival, stats
mets (>= 1.1), optimx, data.table, Rgraphviz, graph, igraph
(>= 0.6), visNetwork, Matrix, KernSmooth, lme4, geepack, gof
(>= 0.9), foreach, quantreg, rgl, fields, testthat (>= 0.11),
ascii, zoo

c(person("Klaus K.", "Holst", email="kkho@biostat.ku.dk", role=c("aut", "cre")),
person("Brice", "Ozenne", role = "ctb"),
person("Thomas", "Gerds", role = "ctb"))
Klaus K. Holst [aut, cre],
Brice Ozenne [ctb],
Thomas Gerds [ctb]

c(person("Klaus K.", "Holst", email="klaus@holst.it", role=c("aut", "cre")),
person("Brice", "Ozenne", role = "ctb"),
person("Thomas", "Gerds", role = "ctb"))
grDevices, graphics, methods, numDeriv, stats, survival, utils
KernSmooth, Matrix, Rgraphviz, ascii, data.table, fields,
foreach, geepack, gof (>= 0.9), graph, igraph (>= 0.6),
lava.tobit, lme4, mets (>= 1.1), optimx, quantreg, rgl,
testthat (>= 0.11), visNetwork, zoo

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>). 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.