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Version 0.5 (in progress)
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- Add support for fitting an intercept in :ref:`SqrtLasso <skglm.experimental.sqrt_lasso.SqrtLasso>` (PR: :gh:`298`)
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- Add experimental :ref:`QuantileHuber <skglm.experimental.quantile_huber.QuantileHuber>` and :ref:`SmoothQuantileRegressor <skglm.experimental.quantile_huber.SmoothQuantileRegressor>` for quantile regression, and an example script (PR: :gh:`312`).
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- Add :ref:`GeneralizedLinearEstimatorCV <skglm.cv.GeneralizedLinearEstimatorCV>` for cross-validation with automatic parameter selection for L1 and elastic-net penalties (PR: :gh:`299`)
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- Add :class:`skglm.datafits.group.PoissonGroup` datafit for group-structured Poisson regression. (PR: :gh:`317`)
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- Add :ref:`GraphicalLasso <skglm.covariance.GraphicalLasso>` for sparse inverse covariance estimation with both primal and dual algorithms
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- Add :ref:`AdaptiveGraphicalLasso <skglm.covariance.AdaptiveGraphicalLasso>` for non-convex penalty variations using iterative reweighting strategy
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- Add support for fitting an intercept in :ref:`SqrtLasso <skglm.experimental.SqrtLasso>` (PR: :gh:`298`)
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- Add experimental :ref:`QuantileHuber <skglm.experimental.QuantileHuber>` and :ref:`SmoothQuantileRegressor <skglm.experimental.SmoothQuantileRegressor>` for quantile regression (PR: :gh:`312`).
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- Add :ref:`GeneralizedLinearEstimatorCV <skglm.GeneralizedLinearEstimatorCV>` for cross-validation with automatic parameter selection for L1 and elastic-net penalties (PR: :gh:`299`)
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