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 (2019) <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.

Latent Variable Models

package-ubuntu-20.04-r-4.0.5#cran#lava-1.6.10

Maximum likelhood estimates of polychoric correlations

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
Variable 1
</td>
</tr>
<tr valign="top">
<td><code>y</code></td>
<td>
Variable 2
</td>
</tr>
<tr valign="top">
<td><code>X</code></td>
<td>
Optional covariates
</td>
</tr>
<tr valign="top">
<td><code>start</code></td>
<td>
Optional starting values
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
Additional arguments to lower level functions
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#lava-1.6.10#docs#pcor

pcor

Description

Usage

Arguments

lava

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`x`	Variable 1
`y`	Variable 2
`X`	Optional covariates
`start`	Optional starting values
`...`	Additional arguments to lower level functions