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update paper
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lbluque committed Oct 12, 2023
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Expand Up @@ -151,18 +151,21 @@ options are implemented. The implemented models are listed below:
## Implemented regression models

The table below shows the regression models that are implemented in `sparse-lm` as well
as available implementations in other Python packages.

| Model | `sparse-lm` | `celer` | `groupyr` | `group-lasso` | `skglm` | `abess` |
|:-----------------------------:|:-----------:|:-------:|:---------:|:--------------:|:-------:|:-------:|
| (Adaptive) Lasso | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| (Adaptive) Group Lasso | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| (Adaptive) Sparse Group Lasso | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| (Adaptive) Ridged Group Lasso | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| Best Subset Selection | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| Ridged Best Subset Selection | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| $\ell_0$ pseudo-norm | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
| $\ell_0\ell_2$ mixed-norm | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ |
as available implementations in other Python packages. $\checkmark$ indicates that the

| Model | `sparse-lm` | `celer` | `groupyr` | `group-lasso` | `skglm` | `abess` |
|:-----------------------------:|:---------------:|:---------:|:-----------:|:------------------:|:----------:|:--------:|
| (Adaptive) Lasso | $\checkmark$️ | $\checkmark$️ | | | $\checkmark$️ | ️ |
| (Adaptive) Group Lasso | $\checkmark$️ | $\checkmark$️ | $\checkmark$️ | $\checkmark$️ | $\checkmark$ | ️ |
| (Adaptive) Sparse Group Lasso | $\checkmark$️ | | $\checkmark$️ | $\checkmark$️ | $\checkmark$ | ️ |
| (Adaptive) Ridged Group Lasso | $\checkmark$️ | | | | $\checkmark$ | |
| Best Subset Selection | $\checkmark$️ | | | | | ️ |
| Ridged Best Subset Selection | $\checkmark$️ | | | | | ️ |
| $\ell_0$ pseudo-norm | $\checkmark$️ | | | | | ️ |
| $\ell_0\ell_2$ mixed-norm | $\checkmark$️ | | | | | |

Note that only `sparse-lm` includes adaptive versions of Lasso estimators. However, some of the third party packages,
notably `skglm` and `abess`, include additional penalties and regression objectives that are not implemented in `sparse-lm`.

## Implemented model selection and composition tools

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