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Dynamic Fit Index Cutoffs For Latent Variable Models

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# dynamic

The goal of dynamic is to simulate fit index cutoffs for latent variable
models that are tailored to the user’s model statement, model type, and
sample size.

This is the counterpart of the Shiny Application,
<a href="https://dynamicfit.app/connect/"
target="_parent">dynamicfit.app</a>. The Shiny app and the R package
will give you the same results. If you are comfortable with R, consider
using the package during high traffic times to reduce server burden.

## Installation

This is the beta version of the package. Please submit bug reports and
issues on GitHub. You can install the released version of dynamic from
[CRAN](https://CRAN.R-project.org) with:

`install.packages("dynamic")`

**Currently, the functions for categorical data (`catOne` and `catHB`),
non-normal continuous data (`nnorOne` and `nnorHB`), and Likert data treated as continuous
(`likertOne` and `likertHB`) are only available from the GitHub package
and are not yet available on CRAN or the Shiny application.**

To install the version of the package that supports these functions,
use:

``` r
library(devtools)
devtools::install_github("melissagwolf/dynamic")
```

## Example

``` r
library(dynamic)

#Lavaan object example (manual=FALSE)
dat <- lavaan::HolzingerSwineford1939
lavmod <- "F1 =~ x1 + x2 + x3
          F2 =~ x4 + x5 + x6
          F3 =~ x7 + x8 + x9"
fit <- lavaan::cfa(lavmod,dat)
cfaHB(fit)

#Manual entry example (manual=TRUE)
manmod <- "F1 =~ .602*Y1 + .805*Y2 + .857*Y3 + .631*Y4 + .345*Y5 + .646*Y6"
cfaOne(manmod,500,manual=TRUE)
```

## Vignette

A vignette for the multivariate normal functions `cfaOne` and `cfaHB`
can be found [here](https://rpubs.com/melissagwolf/847463).

A vignette for the categorical data functions `catOne` and `catHB` can
be found [here](https://rpubs.com/dmcneish/1025400).

A vignette for non-normal continuous data and/or missing data functions
`nnorOne` and `nnorHB` can be found
[here](https://rpubs.com/dmcneish/1033222)

A vignette for functions`likertOne` and `likertHB` for treating Likert
responses as continuous can be found
[here](https://rpubs.com/dmcneish/1062525)

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