Use this when you want to pass a name as a string and avoid the
unquoted-symbol capture behaviour of morie_describe.
Value
Invisibly returns the narrative as a character scalar.
If no matching describe entry is found, returns NULL
and prints a helpful diagnostic.
Examples
morie_describe_by_name("aalen")
#> # describe('aalen') — aalen
#>
#> ## WHAT IT DOES
#>
#> Aalen additive hazards model.
#>
#> ## WHEN TO USE
#>
#> This callable applies when you have inputs of the appropriate shape and
#> want the documented output. Read the function's source for the canonical
#> signature and assumptions.
#>
#> ## WHEN NOT TO USE
#>
#> - Inputs violate the function's assumed domain (NaN/Inf, wrong shape).
#> - A more specialised version exists for your data shape — see
#> `cheatsheet()` or `_registry.py` for related callables.
#> - Sample size too small for the asymptotics this estimator relies on.
#>
#> ## ASSUMPTIONS
#>
#> - Inputs are real-valued and free of NaN/Inf.
#> - Observations are independent unless noted.
#> - See the FORMULA section in the source code for distributional
#> specifics.
#>
#> ## FORMULA
#>
#> See the function's docstring `Formula:` line in
#> `fn/aalen.py`.
#>
#> ## INPUTS / OUTPUTS
#>
#> Inputs: documented in the function's `Parameters` block.
#> Outputs: a RichResult (dict-subclass) — `result["statistic"]`,
#> `.get(...)`, `for k in result` all work alongside the multi-section
#> `print(result)` render. See `morie.fn.describe('aalen')` for guidance.
#>
#> ## WORKED EXAMPLE
#>
#> ```python
#> from morie.fn.aalen import *
#> import numpy as np
#> # See the function signature in fn/aalen.py for argument names.
#> ```
#>
#> ## COMMON MISTAKES
#>
#> - Treating the result as a plain dict — it IS a dict (RichResult
#> inherits from dict) but `print(result)` shows the multi-section
#> render which is what you usually want.
#> - Ignoring warnings/interpretation when sample sizes are small.
#>
#> ## REFERENCES
#>
#> See the source file's `References` block.
morie_describe_by_name("morie_aalen")
#> # describe('aalen') — aalen
#>
#> ## WHAT IT DOES
#>
#> Aalen additive hazards model.
#>
#> ## WHEN TO USE
#>
#> This callable applies when you have inputs of the appropriate shape and
#> want the documented output. Read the function's source for the canonical
#> signature and assumptions.
#>
#> ## WHEN NOT TO USE
#>
#> - Inputs violate the function's assumed domain (NaN/Inf, wrong shape).
#> - A more specialised version exists for your data shape — see
#> `cheatsheet()` or `_registry.py` for related callables.
#> - Sample size too small for the asymptotics this estimator relies on.
#>
#> ## ASSUMPTIONS
#>
#> - Inputs are real-valued and free of NaN/Inf.
#> - Observations are independent unless noted.
#> - See the FORMULA section in the source code for distributional
#> specifics.
#>
#> ## FORMULA
#>
#> See the function's docstring `Formula:` line in
#> `fn/aalen.py`.
#>
#> ## INPUTS / OUTPUTS
#>
#> Inputs: documented in the function's `Parameters` block.
#> Outputs: a RichResult (dict-subclass) — `result["statistic"]`,
#> `.get(...)`, `for k in result` all work alongside the multi-section
#> `print(result)` render. See `morie.fn.describe('aalen')` for guidance.
#>
#> ## WORKED EXAMPLE
#>
#> ```python
#> from morie.fn.aalen import *
#> import numpy as np
#> # See the function signature in fn/aalen.py for argument names.
#> ```
#>
#> ## COMMON MISTAKES
#>
#> - Treating the result as a plain dict — it IS a dict (RichResult
#> inherits from dict) but `print(result)` shows the multi-section
#> render which is what you usually want.
#> - Ignoring warnings/interpretation when sample sizes are small.
#>
#> ## REFERENCES
#>
#> See the source file's `References` block.