Operating System: Clear Linux (28100) and Antergos (Linux)

Interface Version: `rstan (Version 2.18.2, GitRev: 2e1f913d3ca3)`

Compiler/Toolkit: gcc 8.3.1 and gcc 8.2.1

Hi!

Here is documentation for `bernoulli_logit_glm`

, yet when I try it in a simple logistic regression model:

```
data {
int<lower=0> N;
int y[N];
matrix[N,4] X;
real mu0;
real mu1;
real sigma0;
real sigma1;
}
parameters {
real beta0;
vector[4] beta1;
}
model {
beta0 ~ normal(mu0, sigma0);
beta1 ~ normal(mu1, sigma1);
y ~ bernoulli_logit_glm(X, beta0, beta1);
// y ~ bernoulli_logit(beta0 + X * beta1);
}
```

I get the error:

Probability function must end in _lpdf or _lpmf. Found distribution family = bernoulli_logit_glm with no corresponding probability function bernoulli_logit_glm_lpdf, bernoulli_logit_glm_lpmf, or bernoulli_logit_glm_log

This comes up both in RStudio (preview) and when trying to compile the model.

I was intending to benchmark this model, and given Bob Carpenter’s statements in this post from October 2017:

Matthijs’s first major commit is a set of GLM functions for negative binomial with log link (2–6 times speedup), normal linear regression with identity link (4–5 times), Poisson with log link (factor of 7) and bernoulli with logit link (9 times). Wow! And he didn’t just implement the straight-line case—this is a fully vectorized implementation as a density…

I’d expect a hefty speed up.