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Written by Nick Tierney on 2024-12-17 at 23:13

{greta.gam} is now on CRAN!

{greta.gam} is another addition to the {greta} universe, that lets you use {mgcv}’s smoother functions and formula syntax to define smooth terms for use in a {greta} model. You can then define your own likelihood to complete the model, and fit it by MCMC.

The {greta.gam} package has been around for some time, and was developed by @millerdl and Nick Golding. Thanks to Nick and David for their hard work on this!

See https://github.com/greta-dev/greta.gam for an example of how to use {greta.gam}

[#]rstats

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Written by Noam Ross on 2024-12-17 at 23:21

@njtierney @millerdl I never got on the greta train but this of course piques my interest. What's the pitch for why or when I would do this in greta over, say, a Stan-based approach using brms? Faster parallelization/GPU usage or big data support? More distributions to make use of?

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Written by David Lawrence Miller on 2024-12-18 at 13:33

@noamross @njtierney some quick thoughts:

1. it's TensorFlow under the hood, meaning that we can use not just HMC (like Stan) but also other optimizers. I guess if you have access to TPUs maybe it's faster, I don't know.

2. no C++ nonsense

3. extension to more complicated models is a bit easier (take yr pop dyn model, parameterise, say K in yr Ricker model using a spline), which in my (not recent) experience is somewhat painful in Stan/TMB/JAGS/Nimble.

hth :)

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Written by Aki Vehtari on 2024-12-19 at 11:50

@millerdl @noamross @njtierney I don't understand the ”no C++ nonsense" comment. Based on GitHub stats, both Greta and TensorFlow are mostly C++

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Toot

Written by David Lawrence Miller on 2024-12-19 at 13:50

@avehtari perhaps I should have said "less C++ nonsense"

@noamross @njtierney

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