This function constructs an object of class `fHMM_model`

, which
contains details about the fitted (hierarchical) Hidden Markov model.

## Usage

```
fHMM_model(
data,
estimate,
nlm_output,
estimation_time,
ll,
lls,
gradient,
inverse_fisher,
decoding
)
# S3 method for class 'fHMM_model'
print(x, ...)
# S3 method for class 'fHMM_model'
residuals(object, ...)
# S3 method for class 'fHMM_model'
summary(object, alpha = 0.05, ...)
# S3 method for class 'fHMM_model'
coef(object, alpha = 0.05, digits = 2, ...)
# S3 method for class 'fHMM_model'
AIC(object, ..., k = 2)
# S3 method for class 'fHMM_model'
BIC(object, ...)
# S3 method for class 'fHMM_model'
nobs(object, ...)
# S3 method for class 'fHMM_model'
logLik(object, ...)
npar(object, ...)
# S3 method for class 'fHMM_model'
npar(object, ...)
# S3 method for class 'fHMM_model'
predict(object, ahead = 5, alpha = 0.05, ...)
```

## Arguments

- data
An object of class

`fHMM_data`

.- estimate
A

`numeric`

vector of unconstrained model estimates.- nlm_output
The output of

`nlm`

for the selected optimization run.- estimation_time
A

`diff.time`

object, the total estimation time.- ll
A

`numeric`

, the model log-likelihood.- lls
A

`numeric`

vector, the model log-likelihoods in all optimization runs.- gradient
A

`numeric`

vector, the gradient at the optimum.- inverse_fisher
A

`numeric`

vector, the inverse Fisher information for each parameter.- decoding
A

`numeric`

vector, the decoded time series.- x, object
An object of class

`fHMM_model`

.- ...
Currently not used.

- alpha
A

`numeric`

between 0 and 1, the confidence level.- digits
The number of decimal places.

- k
Passed on to

`AIC`

.- ahead
The number of time points to predict ahead.