> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Changelog 0.6

> Release notes for nimbus-bci 0.6 — factories, profiles, paradigm recipes, breaking preset renames.

Public release notes for **nimbus-bci 0.6.0** (2026-09-13). The package ships a full `CHANGELOG.md` in the PyPI sdist; this page is the docs mirror for the product surface.

Upgrading from 0.5? Start with [Migration to 0.6](/python-sdk/migration-0.6).

Also new in docs: [Paradigms & profiles](/personalizer/paradigms) · [Transforms](/personalizer/transforms) · [Research / PEFT](/personalizer/research-peft).

## Highlights

* Cheap online adaptation: head `partial_fit` \~**8–10×** vs sklearn batch refit; \~**22–131×** vs trunk retrain at matched n
* Factories: `for_deployment`, `for_research`, `for_features`, `for_riemann`, `for_p300`, `for_balora`
* Portable profiles + MI / P300 session bundles
* Embedding transforms + opt-in `[train]` PEFT / BaLoRA

## Added

* `fit_calibrated(...)` — fit + pin decision-gate accept rate
* `adapt(...)` / `adapt_async(...)` — adaptation gateway; `BrainStateBatch`
* `export_profile` / `from_profile` / `UserProfile`
* Transforms: `standardize` / `whiten` / `coral` / `affine` / `rpa` / `euclidean_alignment`
* P300 / Riemann / MI recipes — see [Paradigms & profiles](/personalizer/paradigms)
* Session-bundle trust: `trusted=True` + `sidecar_sha256`
* `calibration_status` / `suggest_calibration_trials`
* BaLoRA + trunk PEFT behind `[train]` — see [Research / PEFT](/personalizer/research-peft)
* Metrics: `cohen_kappa`, `roc_auc_score`, `brier_score`, `temperature_scale_logits`
* Optional forgetting `λ\<1` and `prior_fraction=` on LDA/QDA
* `adapt_errp`, `adapt_pseudo`, `fit_tent`, T3A, `fit_population` (research)

## Changed (breaking)

* Presets: `research` → `strict`, `consumer` → `permissive`
* Middleware imports regrouped under `policies/` / `alignment/` / `techniques/` (no shims)
* `scikit-learn >= 1.6`; Python `>=3.11,<3.14`; `[train]` torch `>=2.14`
* sfreq never-declared warning when encoder has `training_sfreq`

## Evidence integrity

Update **cost** is the robust claim (held even where accuracy gates failed). Cite wall-time ratios — details on [Evidence](/personalizer/evidence).

## Next read

<Columns cols={2}>
  <Card title="Migration to 0.6" icon="arrow-right" href="/python-sdk/migration-0.6">
    What to change in your code
  </Card>

  <Card title="Paradigms & profiles" icon="route" href="/personalizer/paradigms">
    MI / P300 / profiles
  </Card>

  <Card title="Installation" icon="download" href="/python-sdk/installation">
    Extras and verify 0.6.0
  </Card>

  <Card title="Quickstart" icon="rocket" href="/python-sdk/quickstart">
    Factory-first path
  </Card>
</Columns>