CRAN/E | lava

lava

Latent Variable Models

Installation

About

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.

Citation lava citation info
kkholst.github.io/lava/
Bug report File report

Key Metrics

Version 1.8.0
R ≥ 3.0
Published 2024-03-05 51 days ago
Needs compilation? no
License GPL-3
CRAN checks lava results

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Maintainer

Maintainer

Klaus K. Holst

klaus@holst.it

Authors

Klaus K. Holst

aut / cre

Brice Ozenne

ctb

Thomas Gerds

ctb

Material

README
NEWS
Reference manual
Package source

In Views

Psychometrics

Vignettes

Estimating partial correlations with lava
The Art of Influence
Non-linear latent variable models and error-in-variable models

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

lava archive

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

Reverse Depends

lavaSearch2
NMMIPW
targeted

Reverse Imports

BuyseTest
FunctanSNP
LMMstar
mets
pec
polle
prodlim
Publish
riskRegression
timereg