Linear and regularized

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Linear and regularized models predict with a weighted sum of features. They run from ordinary least squares through ridge, lasso, and elastic net shrinkage to structured and adaptive penalties that also select variables.

Pass any model string below as Arm(model=...). Extra names an optional dependency, Scaling flags whether predictors should be standardized, and Tunable counts the hyperparameters the search space exposes.

Model string	Description	Input	Extra	Scaling	Recommended preprocessing	Tunable
adaptive_elastic_net	Adaptive elastic net using initial coefficient-based column weights.	supervised	none	no	default	3
adaptive_lasso	Adaptive lasso using initial coefficient-based penalty weights.	supervised	none	no	default	2
bayesian_ridge	Empirical-Bayes Bayesian ridge.	supervised	none	no	default	0
elastic_net	Elastic net regression.	supervised	none	no	default	2
fused_difference_ridge	Ridge regression with a fused-difference coefficient prior.	supervised	none	no	default	1
glmboost	Componentwise linear boosting.	supervised	none	no	default	2
group_lasso	Package-native group lasso with group-level sparsity.	supervised	none	no	default	1
huber	Robust Huber regression.	supervised	none	no	default	1
lasso	Lasso regression.	supervised	none	no	default	1
nonneg_ridge	Ridge regression with non-negative coefficients.	supervised	none	no	default	1
ols	Ordinary least squares with no model-owned tuning space.	supervised	none	no	default	0
random_walk_ridge	Time-varying random-walk ridge fit, predicting with the final coefficient vector.	supervised	none	no	default	1
ridge	Ridge regression.	supervised	none	no	default	1
shrink_to_target_ridge	Ridge regression shrinking coefficients toward a target vector.	supervised	none	no	default	1
sparse_group_lasso	Package-native sparse group lasso with group and feature-level sparsity.	supervised	none	no	default	2
tvp_ridge	Goulet Coulombe TVP ridge / 2SRR estimator.	supervised	none	no	default	1

Reference

• Models reference page for ModelSpec, ModelFit, and fit conventions.