This function sets and checks model parameters. Unspecified parameters are sampled.

## Usage

```
fHMM_parameters(
controls = list(),
hierarchy = FALSE,
states = if (!hierarchy) 2 else c(2, 2),
sdds = if (!hierarchy) "normal" else c("normal", "normal"),
Gamma = NULL,
mu = NULL,
sigma = NULL,
df = NULL,
Gamma_star = NULL,
mu_star = NULL,
sigma_star = NULL,
df_star = NULL,
scale_par = c(1, 1),
seed = NULL,
check_controls = TRUE
)
# S3 method for fHMM_parameters
print(x, ...)
```

## Arguments

- controls
Either a

`list`

or an object of class`fHMM_controls`

.The

`list`

can contain the following elements, which are described in more detail below:`hierarchy`

, defines an hierarchical HMM,`states`

, defines the number of states,`sdds`

, defines the state-dependent distributions,`horizon`

, defines the time horizon,`period`

, defines a flexible, periodic fine-scale time horizon,`data`

, a`list`

of controls that define the data,`fit`

, a`list`

of controls that define the model fitting

Either none, all, or selected elements can be specified.

Unspecified parameters are set to their default values, see below.

Specifications in

`controls`

override individual specifications.- hierarchy
A

`logical`

, set to`TRUE`

for an hierarchical HMM.If

`hierarchy = TRUE`

, some of the other controls must be specified for the coarse-scale and the fine-scale layer.By default,

`hierarchy = FALSE`

.- states
An

`integer`

, the number of states of the underlying Markov chain.If

`hierarchy = TRUE`

,`states`

must be a`vector`

of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer.By default,

`states = 2`

if`hierarchy = FALSE`

and`states = c(2, 2)`

if`hierarchy = TRUE`

.- sdds
A

`character`

, specifying the state-dependent distribution. One of`"normal"`

(the normal distribution),`"lognormal"`

(the log-normal distribution),`"t"`

(the t-distribution),`"gamma"`

(the gamma distribution),`"poisson"`

(the Poisson distribution).

The distribution parameters, i.e. the

mean

`mu`

,standard deviation

`sigma`

(not for the Poisson distribution),degrees of freedom

`df`

(only for the t-distribution),

can be fixed via, e.g.,

`"t(df = 1)"`

or`"gamma(mu = 0, sigma = 1)"`

. To fix different values of a parameter for different states, separate by "|", e.g.`"poisson(mu = 1|2|3)"`

.If

`hierarchy = TRUE`

,`sdds`

must be a`vector`

of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer.By default,

`sdds = "normal"`

if`hierarchy = FALSE`

and`sdds = c("normal", "normal")`

if`hierarchy = TRUE`

.- Gamma, Gamma_star
A transition probability

`matrix`

.It should have dimension

`states[1]`

.`Gamma_star`

is a`list`

of fine-scale transition probability matrices. The`list`

must be of length`states[1]`

. Each transition probability matrix must be of dimension`states[2]`

.- mu, mu_star
A

`numeric`

vector of expected values for the state-dependent distribution in the different states.For the gamma- or Poisson-distribution,

`mu`

must be positive.It should have length

`states[1]`

.`mu_star`

is a`list`

of`vectors`

with fine-scale expectations. The`list`

must be of length`states[1]`

. Each`vector`

must be of length`states[2]`

.- sigma, sigma_star
A positive

`numeric`

vector of standard deviations for the state-dependent distribution in the different states.It should have length

`states[1]`

.`sigma_star`

is a`list`

of`vectors`

with fine-scale standard deviations. The`list`

must be of length`states[1]`

. Each vector must be of length`states[2]`

.- df, df_star
A positive

`numeric`

vector of degrees of freedom for the state-dependent distribution in the different states.It should have length

`states[1]`

.Only relevant in case of a state-dependent t-distribution.

`df_star`

is a`list`

of`vectors`

with fine-scale degrees of freedom. The`list`

must be of length`states[1]`

. Each vector must be of length`states[2]`

. Only relevant in case of a fine-scale state-dependent t-distribution.- scale_par
A positive

`numeric`

vector of length two, containing scales for sampled expectations and standard deviations.The first entry is the scale for

`mu`

and`sigma`

, the second entry is the scale for`mu_star`

and`sigma_star`

(if any).- seed
Sets a seed for the sampling of parameters.

- check_controls
Either

`TRUE`

to check the defined controls or`FALSE`

to not check them (which saves computation time), else.- x
An object of class

`fHMM_parameters`

.- ...
Currently not used.

## Details

See the vignette on the model definition for more details.