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 Carpenterās statements in this post from October 2017:

Matthijsās first major commit is a set of GLM functions for negative binomial with log link (2ā6 times speedup), normal linear regression with identity link (4ā5 times), Poisson with log link (factor of 7) and bernoulli with logit link (9 times). Wow! And he didnāt just implement the straight-line caseāthis is a fully vectorized implementation as a densityā¦

Iād expect a hefty speed up.