# Command Coverage Matrix

Every registered action of both plugins, broken down by parameter group, mapped
to the one chapter that owns it and to the chapters that reuse it.

Look up `--p-stabsel-b` here to find the chapter that explains it. The same rows
feed a CI check: if a plugin registers a parameter that no row claims, the build
should fail.

There are 6 q2-gglasso actions and 8 q2-classo actions. Every one appears below,
together with every parameter (`--p-`) and input (`--i-`, `--m-`) group listed in
[q2-gglasso Parameter Reference](02_gglasso_parameters.md) and
[q2-classo Parameter Reference](03_classo_parameters.md).

```{note}
**Outputs are out of scope.** Each action has a fixed output signature that does
not vary by chapter, so tracking `--o-` flags row by row adds noise without adding
coverage. `--o-group-array` is the one exception listed below, because the
artifact it produces cannot be fed back into `solve-problem` through the type
system — a fact about the workflow rather than about the output. Full output
signatures live in the two reference pages, and CI check 2 below is scoped to
parameters accordingly.
```

## How to read the matrix

Each row is an **action x parameter group**, not an action and not a single
parameter. `--p-stabsel-b`, `--p-stabsel-q` and `--p-stabsel-threshold` interact
too closely to document apart, so they share a row and a primary chapter.

Two columns carry the traceability:

- **Primary** — the single chapter that *introduces* the group, explains why the parameters matter, and is responsible for keeping the explanation correct. Exactly one chapter per
  row. Fix any error about a parameter in its primary chapter first.
- **Also in** — chapters that use the group again at a different scale or on a
  different dataset without re-explaining it. Link back to the primary rather
  than duplicating it.

Tier 1 is meant to be the reference tier: each action gets its canonical
demonstration on the 13-ASV Atacama table, and the later tiers introduce new
*values* and new *questions* rather than new commands. Where a row's primary
chapter is not in tier 1, that is a deliberate exception or an outstanding gap —
{ref}`coverage-debt` lists them all.

### Chapter keys

The matrix uses short keys so the tables stay narrow.

| Key | Chapter |
|---|---|
| `DL` | [Download the Tutorial Data](../00_getting_started/03_download_data.md) |
| `INST-GG` | [Installing q2-gglasso](../01_installation/02_q2_gglasso.md) |
| `INST-CL` | [Installing q2-classo](../01_installation/03_q2_classo.md) |
| `VERIFY` | [Verifying Your Installation](../01_installation/04_verify.md) |
| `G-PREP` | [Data Preparation](../02_lowdim_gglasso/01_data_preparation.md) |
| `G-SGL` | [Single Graphical Lasso](../02_lowdim_gglasso/02_sgl.md) |
| `G-SLR` | [Sparse + Low-Rank](../02_lowdim_gglasso/03_slr.md) |
| `G-ADAPT` | [Adaptive Graphical Lasso](../02_lowdim_gglasso/04_adaptive_glasso.md) |
| `G-PATH` | [Regularization Paths & Model Selection](../02_lowdim_gglasso/05_lambda_paths.md) |
| `G-MGL` | [Multiple Graphical Lasso](../02_lowdim_gglasso/06_multiple_graphical_lasso.md) |
| `G-PCA` | [Latent-Component PCA](../02_lowdim_gglasso/07_pca.md) |
| `G-SUM` | [Summarizing a Solution](../02_lowdim_gglasso/08_summarize.md) |
| `G-INT` | [Interpretation (gglasso)](../02_lowdim_gglasso/09_interpretation.md) |
| `C-GEN` | [Synthetic Data with Known Truth](../03_lowdim_classo/01_generate_data.md) |
| `C-PREP` | [Data Preparation (classo)](../03_lowdim_classo/02_data_preparation.md) |
| `C-REG` | [Log-Contrast Regression](../03_lowdim_classo/03_regression/01_logcontrast.md) |
| `C-RTRAC` | [Regression with trac](../03_lowdim_classo/03_regression/02_trac.md) |
| `C-CLF` | [Log-Contrast Classification](../03_lowdim_classo/04_classification/01_logcontrast.md) |
| `C-CTRAC` | [Classification with trac](../03_lowdim_classo/04_classification/02_trac.md) |
| `C-CONC` | [Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md) |
| `C-MSEL` | [Choosing a Model](../03_lowdim_classo/05_advanced/02_model_selection.md) |
| `C-PRED` | [Predict & Summarize](../03_lowdim_classo/06_predict_and_summarize.md) |
| `C-INT` | [Interpretation (classo)](../03_lowdim_classo/07_interpretation.md) |
| `H-DATA` | [The 300-ASV Dataset](../04_highdim_atacama/01_data.md) |
| `H-LAM` | [Selecting lambda](../04_highdim_atacama/02_model_selection.md) |
| `H-RANK` | [Choosing the Latent Rank](../04_highdim_atacama/03_slr_ranks.md) |
| `H-PCA` | [Latent Components & Covariates](../04_highdim_atacama/04_latent_pca.md) |
| `H-CV` | [Log-Contrast Models at Scale](../04_highdim_atacama/05_classo_cv.md) |
| `H-INT` | [Interpretation (tier 2)](../04_highdim_atacama/06_interpretation.md) |
| `R-GG` | [q2-gglasso Parameter Reference](02_gglasso_parameters.md) |
| `R-CL` | [q2-classo Parameter Reference](03_classo_parameters.md) |
| `R-TS` | [Troubleshooting & Known Failure Modes](04_troubleshooting.md) |

## q2-gglasso

### `transform-features`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Input table | `--i-table` | `G-PREP` | `G-ADAPT`, `G-MGL`, `H-DATA`, `VERIFY` |
| Choice of transform | `--p-transformation` | `G-PREP` | `G-ADAPT`, `G-MGL`, `H-DATA`, `VERIFY` |
| Zero handling | `--p-pseudo-count` | `H-DATA` | — |
| Metadata as network nodes | `--m-sample-metadata-file`, `--p-add-metadata`, `--p-scale-metadata` | `G-PREP` | `G-ADAPT`, `G-MGL`, `H-DATA` |
| Feature relabelling | `--p-keep-original-id` | `H-DATA` | `G-INT` |
| The unused required input | `--i-taxonomy` | `G-PREP` | `DL`, `R-TS` |

`--p-transformation` and the metadata switches change what the network *means*,
so `G-PREP` owns both rather than leaving them scattered. `--i-taxonomy` has a row
of its own because the registration requires it and the function never reads it,
so readers hit it before they hit anything statistical.

### `build-groups`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Instance tables and validation | `--i-tables`, `--p-check-groups`, `--o-group-array` | `G-MGL` | — |
| The `TensorData` -> `List[Int]` gap | (export workaround) | `G-MGL` | `R-GG`, `R-TS` |

```{important}
`build-groups` emits a `TensorData` artifact while `solve-problem` accepts
`group_array` as a `List[Int]` parameter. They do not chain through the QIIME 2
type system, and there is no other route: export the artifact and pass the
indices by hand with `--p-group-array`. This is a known gap in the plugins, and
`G-MGL` is the one chapter responsible for spelling out the workaround.
```

### `calculate-covariance`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Input table (the p x n transform output) | `--i-table` | `G-PREP` | `G-ADAPT`, `G-MGL`, `H-DATA`, `VERIFY` |
| Scaling | `--p-method` | `G-PREP` | `G-ADAPT`, `G-MGL`, `H-DATA`, `VERIFY` |
| Normalisation denominator | `--p-bias` | `H-DATA` | — |

### `solve-problem`

The largest parameter surface in either plugin. Its twenty parameters divide into
eight concerns, and no chapter tries to cover more than two of them at once.

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Input covariance | `--i-covariance-matrix` | `G-SGL` | `G-SLR`, `G-ADAPT`, `G-PATH`, `G-MGL`, `H-LAM`, `H-RANK` |
| Problem size | `--p-n-samples` | `G-SGL` | `G-SLR`, `G-ADAPT`, `G-PATH`, `G-MGL`, `H-LAM`, `H-RANK` |
| Sparsity grid | `--p-lambda1-min`, `--p-lambda1-max`, `--p-n-lambda1` | `G-SGL` | `G-SLR`, `G-ADAPT`, `G-PATH`, `G-MGL`, `H-LAM`, `H-RANK` |
| Explicit grids and spacing | `--p-lambda1-path`, `--p-mu1-path`, `--p-path-scale` | `G-PATH` | `G-MGL`, `H-LAM`, `H-RANK` |
| Low-rank block | `--p-latent`, `--p-mu1-min`, `--p-mu1-max`, `--p-n-mu1` | `G-SLR` | `G-PATH`, `G-PCA`, `H-LAM`, `H-RANK`, `H-PCA` |
| Explicit rank (always raises) | `--p-rank` | `H-RANK` | `G-PATH`, `G-PCA`, `R-TS` |
| Adaptive penalty weights | `--p-weights` | `G-ADAPT` | — |
| Multiple instances | `--p-reg`, `--p-lambda2-min`, `--p-lambda2-max`, `--p-n-lambda2`, `--p-non-conforming`, `--p-group-array` | `G-MGL` | `G-PATH`, `VERIFY` |
| Model-selection criterion | `--p-gamma` | `G-PATH` | `G-SGL`, `G-SLR`, `G-ADAPT`, `G-MGL`, `H-LAM`, `H-RANK` |

```{important}
Two behaviours cut across the grid rows above, and getting either wrong produces
a plausible-looking result rather than an error. Restate both wherever a chapter
sets a grid.

**Defaults appear silently.** Leaving a grid entirely unset substitutes a
built-in path and emits a warning. Setting only one bound substitutes the other
one with no warning at all.

**Model selection runs only if at least one grid has more than one value.** For a
latent problem, `lambda1`, `lambda2` *and* `mu1` must all be singletons before the
run counts as a single fit. `G-PATH` owns the full explanation; `H-LAM` and
`H-RANK` reuse it.
```

`--p-rank` always raises on every released GGLasso up to and including
0.3.0 — `ValueError` if `--p-latent` is not set, `NotImplementedError`
otherwise — because no release can fix the rank of the low-rank component.
`H-RANK` documents the alternative: steer the rank through `mu1`, where a larger
`mu1` gives a smaller rank, and read the achieved rank back out of the solution.

### `pca` (visualizer)

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Inputs and the required metadata file | `--i-table`, `--i-solution`, `--m-sample-metadata-file` | `G-PCA` | `H-PCA`, `R-TS` |
| Projection and colouring | `--p-n-components`, `--p-color-by` | `G-PCA` | `H-PCA` |

```{note}
`pca` has two prerequisites the signature does not state.

Solve with `--p-latent True` first — the visualizer reads `solution/lowrank_`,
which a sparse-only SGL solution does not have.

Pass `--m-sample-metadata-file` even though the signature marks it optional: the
visualizer dereferences it unconditionally, so omitting it crashes with an
`AttributeError`. `G-PCA` states both before its first command.
```

### `summarize` (visualizer)

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Input solution | `--i-solution` | `G-SUM` | `G-SGL`, `G-SLR`, `G-ADAPT`, `G-PATH`, `H-RANK` |
| Label sizing | `--p-label-size` | `G-SUM` | `G-SGL`, `G-SLR`, `G-ADAPT`, `G-PATH`, `H-RANK` |
| Canvas size | `--p-width`, `--p-height` | `G-SUM` | `H-RANK` |
| Covariate block separation | `--p-n-cov` | `G-SUM` | `G-ADAPT`, `G-INT`, `H-DATA` |

`--p-n-cov` pairs with `--p-add-metadata`: it tells the heatmaps how many
*trailing* variables are covariates rather than taxa, so the two blocks cluster
separately. Set it if you turned metadata into nodes in `G-PREP`.

## q2-classo

### `generate-data`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Problem shape | `--p-n`, `--p-d`, `--p-d-nonzero` | `C-GEN` | — |
| Response type | `--p-classification` | `C-GEN` | — |
| Taxonomy-derived labels and tree | `--i-taxa` | `C-GEN` | — |
| The `randomy.tsv` side effect | (no flag) | `C-GEN` | `R-CL` |

```{note}
`generate-data` writes `randomy.tsv` into the current working directory — the
generated response is not returned as an artifact — and overwrites it on every
call. `C-GEN` is the only chapter that runs this action, and it says where to run
it from.
```

### `transform-features`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Input features | `--i-features` | `C-PREP` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC` |
| CLR transform and pseudocount | `--p-transformation`, `--p-coef` | `C-PREP` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC` |

This is a different implementation from `qiime gglasso transform-features`:
`coef` rather than `pseudo_count`, no `mclr`, no metadata handling, and a
sample-major output because `regress` wants samples in rows. The shared action
name is the most common source of confusion between the two plugins, and `C-PREP`
says so explicitly.

### `add-taxa`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Tree change of basis | `--i-features`, `--i-weights`, `--i-taxa` (no parameters) | `C-RTRAC` | `C-PREP`, `C-CTRAC`, `C-PRED` |

### `add-covariates`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Inputs, column selection and one-hot expansion | `--i-features`, `--i-c`, `--i-weights`, `--m-covariates-file`, `--p-to-add` | `C-PREP` | `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `H-CV` |
| Per-covariate penalty weight | `--p-w-to-add` | `C-PREP` | `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `H-CV` |
| Rescaling numeric covariates | `--p-rescale` | `H-CV` | — |

Categorical columns are expanded to one-hot indicators labelled
`<name> = <value>`, spaces included. Those labels appear in the `summarize`
coefficient plots, so one categorical covariate contributes several rows to the
output. `C-PREP` owns that fact.

### `regress`

The four model-selection procedures — PATH, CV, StabSel, LAMfixed — are all on by
default, and each has its own prefix and its own numerical method. They are four
parallel blocks over the same fitted path.

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Inputs | `--i-features`, `--i-c`, `--i-weights` | `C-REG` | `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-CONC`, `C-MSEL`, `H-CV` |
| Numeric response | `--m-y-file`, `--m-y-column` | `C-REG` | `C-RTRAC`, `C-CONC`, `C-MSEL`, `H-CV` |
| Response shift | `--p-do-yshift` | `C-MSEL` | `H-CV` |
| Intercept | `--p-intercept` | `C-MSEL` | `C-PRED`, `H-CV` |
| Loss and noise model | `--p-concomitant`, `--p-huber`, `--p-rho` | `C-CONC` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-PRED`, `H-CV` |
| PATH | `--p-path`, `--p-path-nlam-log`, `--p-path-lamin-log`, `--p-path-n-active`, `--p-path-numerical-method` | `C-MSEL` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-PRED`, `H-CV` |
| CV | `--p-cv`, `--p-cv-subsets`, `--p-cv-nlam`, `--p-cv-lamin`, `--p-cv-logscale`, `--p-cv-one-se`, `--p-cv-seed`, `--p-cv-numerical-method` | `C-MSEL` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-PRED`, `H-CV` |
| Deprecated CV alias | `--p-cv--nlam` | `C-MSEL` | `R-CL`, `R-TS` |
| StabSel | `--p-stabsel`, `--p-stabsel-b`, `--p-stabsel-q`, `--p-stabsel-threshold`, `--p-stabsel-threshold-label`, `--p-stabsel-seed`, `--p-stabsel-method`, `--p-stabsel-lam`, `--p-stabsel-true-lam`, `--p-stabsel-lamin`, `--p-stabsel-percent-ns`, `--p-stabsel-numerical-method` | `C-MSEL` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-PRED`, `H-CV` |
| LAMfixed | `--p-lamfixed`, `--p-lamfixed-lam`, `--p-lamfixed-true-lam`, `--p-lamfixed-numerical-method` | `C-MSEL` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `H-CV` |

```{note}
`--p-cv--nlam` — two dashes — is not a typo. The parameter was originally
registered as `cv__nlam` with a double underscore, which QIIME 2 renders
literally. `--p-cv-nlam` is the current spelling; the old one still works and
emits a `DeprecationWarning`. Use `--p-cv-nlam` in new commands, and confine the
deprecated form to the places where it is being explained.
```

### `classify`

`classify` shares the PATH, CV, StabSel and LAMfixed blocks with `regress`, under
the same names and the same defaults. Only the differences are owned separately.

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Categorical response | `--m-y-file`, `--m-y-column` | `C-CLF` | `C-GEN`, `C-CTRAC`, `C-CONC` |
| Hinge loss and its transition point | `--p-huber`, `--p-rho` | `C-CLF` | `C-CTRAC`, `C-CONC` |
| Intercept | `--p-intercept` | `C-CLF` | `C-MSEL` |
| What `classify` does **not** have | (`--p-concomitant`, `--p-do-yshift`) | `C-CONC` | `C-CLF`, `R-CL`, `R-TS` |
| Selection procedures | as `regress` | `C-MSEL` | `C-CLF`, `C-CTRAC` |

```{important}
**`qiime classo classify --p-concomitant` does not exist.** The parameter is not
registered on `classify`, and the solver forces the concomitant formulation off
for classification problems regardless. Passing the flag is a command-line error.
Use the Huber hinge loss instead — `--p-huber True` with an explicit `--p-rho`,
since `rho` defaults to `0.0` here rather than the `1.345` used by `regress`.
`C-CONC` owns this comparison.
```

### `predict`

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Inputs (no parameters) | `--i-features`, `--i-problem` | `C-PRED` | `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-MSEL` |

`predict` emits one prediction set per model selection present in the problem, so
switching CV or StabSel off at fit time silently reduces what you get back.
`C-PRED` explains the coupling.

### `summarize` (visualizer)

| Parameter group | Flags | Primary | Also in |
|---|---|---|---|
| Inputs | `--i-problem`, `--i-taxa`, `--i-predictions` | `C-PRED` | `C-GEN`, `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-MSEL` |
| Plot truncation | `--p-maxplot` | `C-PRED` | `C-GEN`, `C-MSEL` |

## Supporting QIIME 2 commands

These commands are not part of either plugin, but a reader who skips them cannot
complete the chapters, so each has an owning chapter below.

| Command | What the tutorial uses it for | Primary | Also in |
|---|---|---|---|
| `qiime sample-classifier split-table` | Train/test split before `regress` or `classify` (`--p-test-size`, `--p-random-state`, `--p-stratify`) | `C-PREP` | `C-REG`, `C-RTRAC`, `C-CLF`, `C-CTRAC`, `C-PRED` |
| `qiime feature-table filter-features` | Restricting a table to a shared feature set before building multiple graphical-lasso instances | `G-MGL` | — |
| `qiime feature-table filter-samples` | Splitting one table into the K per-group instances (`--p-where`) | `G-MGL` | — |
| `qiime feature-table summarize` | Reading off the sample count that `--p-n-samples` needs | `G-PATH` | — |
| `qiime metadata tabulate` | Inspecting a grouping variable before splitting on it | `G-MGL` | — |
| `qiime tools export` | The `build-groups` workaround, and reading the achieved rank out of a solution | `G-MGL` | `H-RANK` |
| `qiime tools view` | Opening a `.qzv` without a browser round-trip | `G-SUM` | `G-PCA` |
| `qiime tools peek` | Confirming a downloaded artifact's type and UUID | `DL` | `VERIFY` |
| `qiime dev refresh-cache` | Making a freshly installed plugin visible to the CLI | `INST-GG` | `INST-CL`, `VERIFY` |
| `qiime gglasso --help`, `qiime classo --help` | The authoritative parameter list on *your* install | `VERIFY` | `INST-GG`, `INST-CL` |

```{tip}
`qiime feature-table summarize` earns its place because `--p-n-samples` is the
only `solve-problem` parameter with no default, and the value you give passes
straight through as the sample size `N` of the underlying problem — the same `N`
the model-selection criterion is computed against. Read it off the table rather
than from memory.
```

(coverage-debt)=
## Coverage debt

Rows whose primary chapter is not in tier 1, i.e. where tier 1 does not give the
group its canonical demonstration. Each is either a justified exception or work
outstanding.

| Group | Current primary | Why, or what is missing |
|---|---|---|
| `--p-pseudo-count` | `H-DATA` | Not exercised in tier 1. Zero handling only becomes visible on a sparse table, but tier 1 should still name it. **Outstanding.** |
| `--p-keep-original-id` | `H-DATA` | Only the default (`True`) is demonstrated: tier 2 keeps real feature IDs because at 300 features identity matters, `ASV-k` is a position rather than an identifier, and only real IDs join to taxonomy. No command in the book passes `False` — tier 1's `ASV-1 … ASV-13` labels come from the hand-written mapping table in [Atacama Soil Microbiome](../00_getting_started/02_datasets.md), not from this parameter. A short `False` demonstration, next to the warning that its labels are positional, would close this. **Outstanding.** |
| `--p-bias` | `H-DATA` | `N` versus `N-1` changes nothing structural about the network. It is discussed once, in the chapter where the covariance estimate itself is under scrutiny. Justified. |
| `--p-rescale` | `H-CV` | Not exercised in tier 1, although the tier 1 covariates (`elevation`, `ph`) are exactly the case that needs it. **Outstanding.** |
| `--p-rank` | `H-RANK` | The parameter always raises, so its owning chapter is the one about choosing a rank the working way. Justified. |

```{note}
Most of this book has not yet been re-run against QIIME 2 2026.7, so the "Also in"
columns record where a flag is *written*, not where it has been *observed to
work*. The distinction disappears once the CI checks below run.
```

## Checks worth wiring

The two parameter reference pages are meant to be generated rather than written:
capture `qiime gglasso <action> --help` and `qiime classo <action> --help` into
`docs/_data/help/<plugin>-<action>.txt` at build time and render them with
`{literalinclude}`. Once the flag list on those pages comes from the plugin rather
than from a human, both sides of every comparison are mechanical and the matrix
becomes machine-checkable.

In rough order of value:

1. **No invented flags.** Every `--p-`, `--i-`, `--o-` and `--m-` token appearing
   in a fenced `bash` block anywhere under `docs/chapters/` must appear in one of
   the captured help files. This catches a documented parameter that does not
   exist.
2. **No missing parameters.** Every `--p-`, `--i-` and `--m-` flag in a captured
   help file must appear in at least one row of this matrix. This turns coverage
   from a claim into a build failure. Scoped to parameters and inputs; the
   reference pages carry the outputs.
3. **Primaries resolve.** Every chapter key used in a Primary cell must resolve
   to a file listed in `docs/_toc.yml`, and each row must name exactly one.
4. **Reference pages agree with the matrix.** The chapter named in the
   *Demonstrated in* column of `R-GG` and `R-CL` must appear in this matrix as
   either the primary or an "Also in" chapter for that parameter's group.
5. **Deprecated spellings stay quarantined.** `--p-cv--nlam` must not appear in a
   runnable command outside `C-MSEL`, `R-CL` and `R-TS`, where it is being
   explained rather than recommended.
6. **Links resolve.** Every relative link in every chapter points at a file that
   exists — cheap, and the failure mode readers notice first.

```{important}
**None of this is wired up yet.** The `--help` capture step is not wired into the
build, and the parameter reference pages are maintained by hand in the meantime.
Until that changes, treat
`qiime <plugin> <action> --help` on your own installation as the final authority,
the matrix as the intent, and any disagreement between them as a bug worth
filing.
```

## See also

- [q2-gglasso Parameter Reference](02_gglasso_parameters.md) — every flag, type
  and default for the 6 gglasso actions.
- [q2-classo Parameter Reference](03_classo_parameters.md) — the same for the 8
  classo actions, organised by model-selection procedure.
- [Troubleshooting & Known Failure Modes](04_troubleshooting.md) — the traps named
  above, with the symptom you will see.