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

<table summary="R argblock">
<tr valign="top">
<td><code>s</code></td>
<td>
<p>The input vector (of arbitrary class)</p>
</td>
</tr>
<tr valign="top">
<td><code>x</code></td>
<td>
<p>The elements to transform into <code>NA</code> resp. what to transform
<code>NA</code> into.</p>
</td>
</tr>
</table>

<p>A vector with same dimension and class as <code>s</code>.
</p>

##' 
x2NA(1:10, 1:5)
NA2x(x2NA(c(1:10),5),5)##'

package-ubuntu-20.04-r-4.0.5#cran#lava-1.6.10#docs#NA2x

`s`	The input vector (of arbitrary class)
`x`	The elements to transform into `NA` resp. what to transform `NA` into.

NA2x

Description

Usage

Arguments

Value

Author(s)

Examples

lava

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