I hadn’t thought about including an ordered logit distribution, but it’s available in JAGS, Stan etc. so definitely worth including - thanks for the prompt!

As a stopgap, you can define the model using a categorical distribution and some manipulation of inputs. Here’s a first pass at an R function that wraps that all up. I will add an issue on Github to write up a more efficient tensorflow version.

```
# ordered logit model in greta
library(greta)
# First pass at an ordered logit distribution.
# phi is a column vector of logit-effects
# a is a k-1, increasing column vector of logit-cutoffs
# the output is a one-hot-encoded matrix, with each row an observation
ordered_logit <- function(phi, a, n_realisations = NULL) {
as.greta_array <- .internals$greta_arrays$as.greta_array
# coerce to greta arrays
a <- as.greta_array(a)
phi <- as.greta_array(phi)
# get number of realisations (check_multivariate_dims doesn't work here
# because both are vectors)
if (!is.null(n_realisations)) {
n <- n_realisations
} else {
n <- nrow(phi)
}
# replicate phi if it's a scalar
if (nrow(phi) == 1 & n != 1) {
phi <- rep(phi, n)
}
# build matrix where element i,j is phi[i] - a[j]
km1 <- length(a)
phi <- do.call(cbind, replicate(km1, phi, simplify = FALSE))
eta <- sweep(phi, 2, a, "-")
# convert to logits
p <- ilogit(eta)
# get cumulative probabilities
probs <- cbind(rep(1, n), p) - cbind(p, rep(0, n))
# return categorical distribution
categorical(probs)
}
```

Here’s a worked example based on the section in the rethinking book:

```
# test on rethinking problem
library(rethinking)
data(Trolley)
d <- Trolley[1:1000, ]
k <- 7
n <- nrow(d)
# make one-hot-encoded response vector
resp <- matrix(0, n, k)
resp[cbind(1:n, d$response)] <- 1
# unordered vector of cutpoints (bit flaky and needs increasing starting values
# to be provided)
# cutpoints <- normal(0, 10, dim = k - 1)
# make an ordered vector of cutpoints (the priors become a bit tricky here though)
cutpoints_raw <- c(normal(0, 10), normal(0, 1, truncation = c(0, Inf), dim = k - 2))
cutpoints <- cumsum(cutpoints_raw)
phi <- 0
distribution(resp) <- ordered_logit(phi, cutpoints, n_realisations = n)
m <- model(cutpoints)
draws <- mcmc(m) # , initial_values = initials(cutpoints = -2:3)
```

Comparison with the rethinking/stan version:

```
m2 <- map2stan(
alist(
response ~ dordlogit(phi, cutpoints),
phi <- 0,
cutpoints ~ dnorm(0, 10)
),
data = list(response = d$response),
start = list(cutpoints = c(-2, -1, 0, 1, 2, 2.5)),
chains = 4, cores = 4
)
summary(draws)$statistics
precis(m2, depth = 2)
```

I get very similar results to stan/rethinking, so I think this implementation works!