These use the same parametrizations as defined in the 'Stan' documentation. See the docs for gamma and inverse gamma distributions.

uniform(square = FALSE)

normal(mu, sigma, square = FALSE)

student_t(nu, square = FALSE)

gam(shape, inv_scale, square = FALSE)

igam(shape, scale, square = FALSE)

log_normal(mu, sigma, square = FALSE)

bet(a, b)

## Arguments

square is prior for a square-transformed parameter? mean standard deviation degrees of freedom shape parameter (alpha) inverse scale parameter (beta) scale parameter (beta) shape parameter shape parameter

## Value

a named list

Other functions related to the inverse-gamma distribution: dinvgamma_stanlike(), plot_invgamma()

## Examples

# Log-normal prior
log_normal(mu = 1, sigma = 1)
#> $dist #> [1] "log-normal" #> #>$square
#> [1] FALSE
#>
#> $mu #> [1] 1 #> #>$sigma
#> [1] 1
#>
# Cauchy prior
student_t(nu = 1)
#> $dist #> [1] "student-t" #> #>$square
#> [1] FALSE
#>
#> $nu #> [1] 1 #> # Exponential prior with rate = 0.1 gam(shape = 1, inv_scale = 0.1) #>$dist
#> [1] "gamma"
#>
#> $alpha #> [1] 1 #> #>$beta
#> [1] 0.1
#>
#> $square #> [1] FALSE #> # Create a similar priors as in LonGP (Cheng et al., 2019) # Not recommended, because a lengthscale close to 0 is possible. a <- log(1) - log(0.1) log_normal(mu = 0, sigma = a / 2) # for continuous lengthscale #>$dist
#> [1] "log-normal"
#>
#> $square #> [1] FALSE #> #>$mu
#> [1] 0
#>
#> $sigma #> [1] 1.151293 #> student_t(nu = 4) # for interaction lengthscale #>$dist
#> [1] "student-t"
#>
#> $square #> [1] FALSE #> #>$nu
#> [1] 4
#> igam(shape = 0.5, scale = 0.005, square = TRUE) # for sigma
#> $dist #> [1] "inv-gamma" #> #>$alpha
#> [1] 0.5
#>
#> $beta #> [1] 0.005 #> #>$square
#> [1] TRUE
#>