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Parsnip wrappers for mixed-level and hierarchical models

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The [gpboost](https://cran.r-project.org/web/packages/gpboost/) package on CRAN by @fabsig explains itself as such: > **Combining Tree-Boosting with Gaussian Process and Mixed Effects Models** > An R package that allows for combining tree-boosting...

feature

Thanks for creating a tidymodels interface for multilevel models. It would be great to also add regularized multilevel regressions, such as glmmLasso.

feature

part of #56

upkeep

Hello, I have a new model request: Mixed effect random forest (MERF). There are a few packages that support them in R. I'm not sure if this is the proper...

feature

I am having trouble using the `tune::fit_resamples()` function on a `lmer` model (from the `multilevelmod` package). In particular, it looks like that when the predictions for the assessment set are...

bug

Thanks a lot for this package, it's a great addition to `tidymodels`! I've been following the conversation on multilevel models and `tidymodels` somewhere else I can't remember and I think...

documentation

I am interested in fitting a spatial multilevel model using the tidymodels framework. In the [Spatial Regression](https://r-spatial.org/book/16-SpatialRegression.html) chapter of [_Spatial Data Science_](https://r-spatial.org/book/) by Edzer Pebesma and Roger Bivand, the authors...

feature

I think a useful addition would be the Linear Quantile Mixed Model. As with quantile regression, it has become a fairly popular go to modelling approach especially in psychology /...

feature

``` r library(multilevelmod) #> Loading required package: parsnip mtcars$vs Error in terms.formula(f, specials = "id_var"): '.' in formula and no 'data' argument ``` Created on 2024-05-26 with [reprex v2.1.0](https://reprex.tidyverse.org) This...