Suggests: mets, graph, Rgraphviz, igraph (>= 0.6), Matrix, lme4,
geepack, gof (>= 0.9), foreach, survival, testthat, ascii
'iid.R' 'transform.R' 'By.R' 'Expand.R' 'zorg.R'

'iid.R' 'transform.R' 'By.R' 'Expand.R' 'zorg.R' 'ztrim.R'

Suggests: mets (>= 0.2.7), graph, Rgraphviz, igraph (>= 0.6), Matrix,
lme4, geepack, gof (>= 0.9), foreach, survival, testthat, ascii
'zcombine.R' 'multinomial.R' 'ordreg.R' 'pcor.R' 'ordinal.R'

Maintainer: Klaus K. Holst <klaus@holst.it>
KernSmooth, lme4, geepack, gof (>= 0.9), foreach, rgl,

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

Imports: numDeriv, methods, graphics, grDevices, utils, stats
Suggests: 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

Suggests: 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),

Imports: numDeriv, methods, graphics, grDevices, utils, survival, stats
Suggests: 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),

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

Authors@R: c(person("Klaus K.", "Holst", email="klaus@holst.it", role=c("aut", "cre")),
Imports: grDevices, graphics, methods, numDeriv, stats, survival, utils
Suggests: 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,

Description: 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)
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