GBarnsley

@model treats an observed variable as a random variable if it's provided by unpacking

2

When I provide values to a Turing model via unpacking from an array or tuple, they don't seem to be recognised and are treated as random variables. See the following...

Looks the interpretation of not specifying an intercept changes depending on whether you is `ModelFrame` or `apply_schema`, see the following code snippet ``` using StatsModels, DataFrames f = @formula(y ~...

Getting the following error from this code: ``` using Distributions quantile(truncated(Chernoff(), lower = 0.1), 0.75) ``` ``` ERROR: UndefVarError: `newton` not defined in `Distributions` ``` Seems to be due to...

See [here](https://search.r-project.org/CRAN/refmans/actuar/html/discretize.html) for an R implementation of this idea. Is there space for a similar function in Distributions? I've put together a package built on top of Distributions [here](https://github.com/GBarnsley/DiscretizeDistributions.jl) that...