Hello,
I want to estimate the parameter of a truncated distribution that is not prebuilt in Stan. In order to understand the process, I follow Annis, Miller, Palmeri (2016) in their approach to userdefine the Exponential distribution. The prebuilt code works fine for me:
exp_prebuilt < stan_model(model_code= '
data {
int N;
vector[N] y;
real L;
}
parameters {
real<lower=0> lambda;
}
model {
real alpha;
real beta;
alpha =1;
beta =1;
lambda ~ gamma(alpha, beta);
for (i in 1:N) {
y[i] ~ exponential(lambda) T[L,];
}
}
')
I then tried to build the exponential distribution by myself, later I want to replace the Exponential with a userdefined function:
exp_user <stan_model(model_code= '
functions{
real newexp_log(vector x, real lam) {
vector[num_elements(x)] prob;
real lprob;
for(i in 1:num_elements(x)){
prob[i] = lam*exp(lam*x[i]);
}
lprob = sum(log(prob));
return lprob;
}
}
data{
int<lower=0> N;
real y[N];
real L;
}
parameters {
real<lower=0> lambda;
}
model {
real alpha;
real beta;
alpha =1;
beta =1;
lambda ~ gamma(alpha, beta);
for (s in 1:N){
y[s] ~ newexp(lambda) T[L,];
}
}
')
I tried to work with this post but am not sure if I implemented it correctly.
I receive two error messages for my code:

No matches for: real ~ newexp(real) Available argument signatures for newexp: vector ~ newexp(real)

Real return type required for probability function.
Any help in solving this problem, explanations, hints are appreciated!