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Last updated: 2026-07-30
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This page is a navigation overview. Current solver, penalty, backend, and inference coverage is maintained in Implemented Methods and the linked model pages.
| Page | Content |
|---|---|
| Loss Functions | Loss definitions and per-sample formulas |
| Solver Algorithms | Public and internal solver implementations |
| Loss × Penalty × Solver Framework | Dispatch logic and compatibility |
| Solver × Penalty Matrix | Explicit solver routing and restrictions |
| Inference API | Covariance, resampling, and inference interfaces |
- Linear Regression
- Ridge
- Lasso
- Elastic Net
- Adaptive Lasso
- SCAD
- MCP
- Logistic Regression
- Poisson Regression
- Generalized Linear Models
- Ordered Logit/Probit
- Quantile Regression
- Robust Regression
| Need | Estimator | Import | Contract |
|---|---|---|---|
| Full Cox fitting, baseline hazards, survival prediction, formula input, and inference | CoxPH |
from statgpu.survival import CoxPH |
Breslow/Efron/Exact ties, delayed entry, (start, stop], strata, robust/cluster covariance, NumPy/CuPy/Torch |
| Select a non-negative L2 penalty by held-out partial likelihood | CoxPHCV |
from statgpu.survival import CoxPHCV |
Uses the canonical Cox semantics during CV and performs a final CoxPH refit |
| Estimate with L1, L2, ElasticNet, SCAD, or MCP | PenalizedCoxPHModel |
from statgpu.linear_model import PenalizedCoxPHModel |
Broad penalty and generic solver path; currently estimation-only and rejects compute_inference=True |
CoxPH(penalty=...) and PenalizedCoxPHModel are not interchangeable aliases.
Use the canonical CoxPH/CoxPHCV path when counting-process features,
stratification, baseline prediction, or statistical inference are required. Use
PenalizedCoxPHModel when the broader penalty family is the primary requirement
and estimation-only output is sufficient.
The Cox model page is the authoritative user-facing source for
Breslow/Efron/Exact ties, delayed-entry and (start, stop] data, strata,
robust/cluster inference, subject-grouped CV, prediction boundaries, and the
NumPy/CuPy/Torch support matrix. Internal module ownership and extension rules
are documented in dev/design/ARCHITECTURE.md.
- ANOVA
- Covariance Estimation
- Panel Data
- Nonparametric Methods
- Kernel Methods
- Spline Basis Functions
- GAM / Semiparametric Models
- Feature Selection
- Knockoffs
- Multiple Testing
- NumPy, CuPy, and Torch are distinct execution backends; explicit device requests do not silently select another backend.
- Backend support may differ by solver, penalty, inference method, and optional dependency. Consult the detailed compatibility matrix instead of relying on a single global count.
- Validation claims are scoped to the exact model, backend, hardware, and commit tested.
- Historical release and benchmark records are evidence snapshots, not current support matrices.