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---
name: parameter_transform_adapter
description: Apply scalar Stan-style constrained and unconstrained transforms with support checks and log-Jacobian values for bounded real parameters.
---
# Parameter Transform Adapter
Use this skill when a Stan parameter contract must be converted between constrained model coordinates and unconstrained inference coordinates. Do not use it to evaluate likelihoods or gradients.
## Inputs
- Parameter contract containing `name`, `lower`, and `upper`.
- Numeric value in constrained or unconstrained coordinates.
## Outputs
- JSON-compatible result with mapped value, validity, diagnostic message, and log absolute Jacobian.
## Workflow
1. Load or define the scalar parameter contract.
2. Use `scripts/parameter_transforms.py` for `constrain`, `unconstrain`, and `roundtrip` operations.
3. Reject constrained values outside support instead of silently clipping.
4. Pass only valid transformed values to score-model evaluators.
## Validation
Run `python tests/test_parameter_transforms.py` from this skill directory.
## Limitations
This skill supports univariate finite-interval real parameters, which is sufficient for the Bernoulli recovery proxy. Extend it before applying to vectors, simplexes, ordered vectors, or covariance matrices.
