Define priors using different different distribution families

prior(family, ...)

## Arguments

family

the prior distribution (see details)

...

see details

## Value

an object of class prior

## Details

### Available distribution families

The following distributions families can be used for the prior

• normal a normal distribution

• student_t a scaled and shifted t-distribution

• cauchy a Cauchy distribution

• uniform a uniform distribution

• point a point

• beta a beta distribution

The parameters that need to be specified will be dependent on the family

### Normal distribution

When family is set to normal then the following parameters may be be set

• mean mean of the normal prior

• sd standard deviation of the normal prior

• range (optional) a vector specifying the parameter range

### Student t distribution

When family is set to student_t then the following parameters may be set

• mean mean of the scaled and shifted t prior

• sd standard deviation of the scaled and shifted t prior

• df degrees of freedom of the scaled and shifted t prior

• range (optional) a vector specifying the parameter range

### Cauchy distribution

When family is set to cauchy then the following parameters may be set

• location the centre of the Cauchy distribution (default: 0)

• scale the scale of the Cauchy distribution

• range (optional) a vector specifying the parameter range

### Uniform distribution

When family is set to uniform then the following parameters must be set

• min the lower bound

• max the upper bound

### Point

When family is set to point then the following parameters may be set

• point the location of the point prior (default: 0)

### Beta

When family is set to beta then the following parameters may be set

• alpha the first shape parameter

• beta the second shape parameter

## Examples

# specify a normal prior
prior(family = "normal", mean = 0, sd = 13.3)
#> Prior
#>   Family
#>     normal
#>   Parameters
#>     mean: 0
#>     sd: 13.3
#>

# specify a half-normal (range 0 to Infinity) prior
prior(family = "normal", mean = 0, sd = 13.3, range = c(0, Inf))
#> Prior
#>   Family
#>     normal
#>   Parameters
#>     mean: 0
#>     sd: 13.3
#>     range: 0 to Inf
#>

# specify a student t prior
prior(family = "student_t", mean = 0, sd = 13.3, df = 79)
#> Prior
#>   Family
#>     student_t
#>   Parameters
#>     mean: 0
#>     sd: 13.3
#>     df: 79
#>

# specify a truncated t prior
prior(family = "student_t", mean = 0, sd = 13.3, df = 79, range = c(-40, 40))
#> Prior
#>   Family
#>     student_t
#>   Parameters
#>     mean: 0
#>     sd: 13.3
#>     df: 79
#>     range: -40 to 40
#>

# specify a cauchy prior
prior(family = "cauchy", location = 0, scale = .707)
#> Prior
#>   Family
#>     cauchy
#>   Parameters
#>     location: 0
#>     scale: 0.707
#>

# specify a half cauchy prior
prior(family = "cauchy", location = 0, scale = 1, range = c(-Inf, 0))
#> Prior
#>   Family
#>     cauchy
#>   Parameters
#>     location: 0
#>     scale: 1
#>     range: -Inf to 0
#>

# specify a uniform prior
prior(family = "uniform", min = 0, max = 20)
#> Prior
#>   Family
#>     uniform
#>   Parameters
#>     min: 0
#>     max: 20
#>

# specify a point prior
prior(family = "point", point = 0)
#> Prior
#>   Family
#>     point
#>   Parameters
#>     point: 0
#>

# specify a beta prior
prior(family = "beta", alpha = 2.5, beta = 3.8)
#> Prior
#>   Family
#>     beta
#>   Parameters
#>     alpha: 2.5
#>     beta: 3.8
#>