I am excited to announce TuringGLM.jl
!
It uses the wellknown @formula
macro from JuliaStats
ecosystem (StatsModels.jl
, GLM.jl
and MixedModels.jl
) to specify a Bayesian model and returns a ready to be sampled/run Turing.jl
model.
It is still a proofofconcept package and many functionalities are still in the roadmap.
It is also heavily inspired by python’s bambino (PyMC) and R’s brms (Stan).
@formula
The syntax is done by using the @formula
macro and then specifying the dependent variable followed by a tilde ~
then the independent variables separated by a plus sign +
.
Example:
@formula(y ~ x1 + x2 + x3)
Moderations/interactions can be specified with the asterisk sign *
, e.g. x1 * x2
. This will be expanded to x1 + x2 + x1:x2
, which, following the principle of hierarchy, the main effects must also be added along with the interaction effects. Here x1:x2
means that the values of x1
will be multiplied (interacted) with the values of x2
.
Randomeffects (a.k.a. grouplevel effects) can be specified with the (term  group)
inside the @formula
, where term
is the independent variable and group
is the categorical representation (i.e., either a column of String
s or a CategoricalArray
in data
). You can specify a randomintercept with (1  group)
.
Example:
@formula(y ~ (1  group) + x1)
Data
TuringGLM supports any Tables.jl
compatible data interface. The most popular ones are DataFrame
s and NamedTuple
s.
Supported Models
TuringGLM supports nonhiearchical and hierarchical models. For hierarchical models, only single randomintercept hierarchical models are supported.
For likelihoods, TuringGLM.jl
supports:

Gaussian()
(the default if not specified): linear regression 
Student()
: robust linear regression 
Logistic()
: logistic regression 
Pois()
: Poisson count data regression 
NegBin()
: negative binomial robust count data regression
Tutorials
We have several tutorials in the documentation at Home · TuringGLM.jl.
Acknowledgements
I would like to thank the whole TuringGLM.jl
dev team, specially @yebai for the support and trust. Also, @rikh was fundamental in the whole development and I probably could not have done without him. Thanks!