Defines a self‑describing `Forecaster` base class with clear `fitter`, `forecaster`, and `applier` steps. New predictor classes can be added solely by implementing these methods without touching the dispatcher. The class also handles meta‑glue such as optional warm‑start and auto‑warning for incomplete forecasts. Because it only imports standard library utilities (numpy, scipy) and its own sub‐package, it can be extracted for packaging into other forecasting projects.

The `TraceOwnership` class decouples the concept of completed runs from the drawing logic, enabling both matplotlib and plotly backends to share a single schedule for per‑dataset can be retired trails. It supports chunking conditions across synthetic arrays, line segmentation, and `_hyp_forecast_role` tagging without caring about which backend will render it. This is a rare example of lifting a high‑level visual shape to its own object that can be reused in any library that animates time series across multiple groups.

A utility that wraps `importlib` and uses `subprocess` to install an extra layer `pyrat` when an optional dependency is first required. The optional dependency definition is latent in the `pyproject.toml` extras, and the module is only imported after a one‑time `pip install` check. This pattern may raise perturbing installed in an environment with no network, but it effectively lets hypertools present a single API surface while still offloading heavy packages (torch, gensim, plotly) to casual users without explicit install steps. It can be pulled in any project that wants one‑command all‑inclusive installation.