library(rjags)

mod_cat <- "model{

# for(i in 1:n){
# y[i,1:2] ~ dmnorm.vcov(mu[], Sigma[1:2,1:2])
# }


mu[1] <- 0
mu[2] <- 0

IndA ~ dcat(PInd[])

PInd[1] <- 0.33333
PInd[2] <- 0.33333
PInd[3] <- 0.33333


z ~ dnorm(0, pow(0.5, -2))T(T1[IndA],T2[IndA])
rho <- tanh(z)

sigma_11 ~  dgamma(1, 1/5)
sigma_22 ~  dgamma(1, 1/5)

Sigma[1,1] <- pow(sigma_11, 2)
Sigma[2,2] <- pow(sigma_22, 2)

Sigma[1,2] <- (rho[1] * sigma_11 * sigma_22)
Sigma[2,1] <- Sigma[1,2]

TauM[1] <- pow(0.001, -2);
TauM[2] <- pow(0.4, - 2);

T1[1] <- -0.01
T1[2] <- 0
T1[3] <- -1

T2[1] <- 0.01
T2[2] <- 1
T2[3] <- 0

}"


fit <- jags.model(textConnection(mod_cat))

codaSamples = coda.samples(fit,
                           variable.names=c("IndA", "rho"),
                           n.iter= 500000)