You can install the released version of Bayesrel from CRAN with:

`install.packages("Bayesrel")`

or install the latest version of Bayesrel from [github] (https://github.com) with the help of the remotes-package:

`::install_github("juliuspfadt/Bayesrel") remotes`

This is a basic example which shows you how to compute alpha, lambda2, the glb, and omega for an example real data set. The output includes both Bayesian and frequentist estimates.

```
library(Bayesrel)
## basic example code
## load example data set from the package
## run the main reliability function
<- strel(data = asrm)
res ## get a full result output
summary(res)
## return the probability that coefficient alpha is larger than .70
pStrel(x = res, estimate = "alpha", low.bound = .70)
## get the posterior median of, e.g., alpha instead of the mean:
median(res$Bayes$samp$Bayes_alpha)
```

This is a basic example which shows you how to compute omega_t and omega_h for an example real data set. The data follow a second-order factor model with no crossloadings:

```
library(Bayesrel)
## basic example code
## run the Bayesian omegas, specify 5 group factors
<- bomegas(data = upps, n.factors = 5, missing = "impute")
res ## get a full result output
summary(res)
## return the probability that coefficient omega_t is larger than .70
pOmegas(x = res, cutoff.t = .70)
## plot posterior predictive check for the higher-order (second-order) factor model
multiFit(x = res, data = upps)
```

In the example above we implicitly assumed that the items of the data set were ordered so that, with 5 group factors, the first four items load on the first factor, items 5-8 load on the second factor and so on. When the data is not organized this way and/or the items cannot be distributed among the factors evenly, one can specify a model syntax relating the items to the group factors in lavaan style. The item names need to equal the variable names in the data:

```
<- "
model f1 =~ U17_r + U22_r + U29_r + U34_r
f2 =~ U4 + U14 + U19 + U27
f3 =~ U6 + U16 + U28 + U48
f4 =~ U23_r + U31_r + U36_r + U46_r
f5 =~ U10_r + U20_r + U35_r + U52_r
"
<- bomegas(data = upps, n.factors = 5, model = model, missing = "impute") res
```

If crossloadings are to be specified, you need a model syntax file to
pass to the `bomegas`

function. For instance, assume that
item U29_r and U34_r load on f3 and f4, respectively.

```
<- "
model f1 =~ U17_r + U22_r + U29_r + U34_r
f2 =~ U4 + U14 + U19 + U27
f3 =~ U6 + U16 + U28 + U48 + U29_r
f4 =~ U23_r + U31_r + U36_r + U46_r + U34_r
f5 =~ U10_r + U20_r + U35_r + U52_r
"
<- bomegas(data = upps, n.factors = 5, model = model, missing = "impute") res
```

The necessary code to infer omega_t and omega_h from a bi-factor
model is analogue to the second-order model, except that the
`model.type`

changes from the default
`second-order`

to `bi-factor`

. Note, that
crossloadings are not permitted in the bi-factor model at this point. If
`model.type = "correlated"`

the correlated factor model is
fit to the data. Crosslaodings are allowed. Only omega_t may be
estimated. The remaining code stays the same as in the examples above.
Which factor model is appropriate for the data is up to theoretical
considerations and model fit.

The frequentist estimation roughly follows the same steps as the Bayesian one. For instance, with the correlated factor model:

```
<- omegasCFA(data = upps, n.factors = 5, model.type = "correlated")
res res
```