Models

A statistical model combines a datafit and penalty. A solver supplies the numerical method; it is selected explicitly rather than inferred from the model's name.

Model	Construction	Prediction
Linear regression	Gaussian with a compatible penalty	Response mean
Logistic regression	Binomial with a compatible penalty	Logit; transform to a probability
Multivariate regression	MultivariateGaussian with AxisPenalty	Response-major means
Lasso convenience API	Named Gaussian L1 configuration	Response mean
SLOPE	Gaussian or binomial with Slope	Response mean or logit
Clustered lasso	Gaussian or binomial with ClusteredLasso	Response mean or logit
Chain fused lasso	Gaussian or binomial with ChainFusedLasso	Response mean or logit

Use Problem::new(&x, datafit, penalty).fit_with(&solver) for typed composition. The type system checks supported combinations. Runtime problems permit dynamic selection with explicit capability checks; see the solver compatibility table.

Lasso is currently the sole named convenience model. Other model families use composition. The shared conventions describe intercepts, normalization, penalty scales, and original-scale output. The Python package exposes Gaussian lasso and composition with Gaussian or binomial datafits, the three MVP penalties, and explicit hybrid or proximal-gradient solvers.

API: shrinkage::Problem and shrinkage::RuntimeProblem.