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Co-authored-by: Trevor James Smith <[email protected]>

CHANGES.rst

@@ -40,7 +40,7 @@ Breaking changes
 * `black` formatting style has been updated to the 2024 stable conventions. `isort` has been added to the `dev` installation recipe. (:pull:`1626`).
 * The indice and indicator for ``winter_storm`` has been removed (deprecated since `xclim` v0.46.0 in favour of ``snd_storm_days``). (:pull:`1565`).
 * `xclim` has dropped support for `scipy` version below v1.9.0 and `numpy` versions below v1.20.0. (:pull:`1565`).
-* `lmoments3` was removed as a dependency of xclim due to its incompatible license (GPLv3 vs xclim's Apache). If we get the approbation from all intellectual property owners of the package, we might put it back. See `Ouranosinc/lmoments3#12 <https://github.com/Ouranosinc/lmoments3/issues/12>`_. See also the "frequency analysis" notebook for an example on how to continue using the probability weighted moments method for fitting distributions. (:issue:`1620`, :pull:`1644`).
+* `lmoments3` was removed as a dependency of `xclim` due to incompatible licensing (GPLv3 vs `xclim`'s Apache 2.0). Depending on the outcome of efforts to modify the licensing of `lmoments3`, this change may eventually be reverted. See `Ouranosinc/lmoments3#12 <https://github.com/Ouranosinc/lmoments3/issues/12>`_. See also the "frequency analysis" notebook for an example on how to continue using the probability weighted moments method for fitting distributions. (:issue:`1620`, :pull:`1644`).
 
 Bug fixes
 ^^^^^^^^^

docs/notebooks/frequency_analysis.ipynb

@@ -105,7 +105,7 @@
    "source": [
     "The next step is to fit the statistical distribution on these maxima. This is done by the `.fit` method, which takes as argument the sample series, the distribution's name and the parameter estimation `method`. The fit is done by default using the Maximum Likelihood algorithm (`method=\"ML\"`). Parameters can also be estimated using the method of moments (`method=\"MM\"`).\n",
     "\n",
-    "xclim can also accept a distribution instance instead of name (i.e. a subclass of `scipy.stats.rv_continuous`). For example, for some extreme value distributions, the maximum likelihood is not always robust. Using the Probability Weighted Moments (`method=\"PWM\"`) method can help in that case. This is possible by passing a distribution object from the `lmoments3` package together with `method=\"PWM\"`. That package currently only supports `expon`, `gamma`, `genextreme`, `genpareto`, `gumbel_r`, `pearson3` and `weibull_min` (with other names, see [the documentation](https://lmoments3.readthedocs.io/en/stable/distributions.html)). In the following example, we fit using the \"Generalized extreme value\" distribution from `lmoments3`."
+    "`xclim` can also accept a distribution instance instead of name (i.e. a subclass of `scipy.stats.rv_continuous`). For example, for some extreme value distributions, the maximum likelihood is not always robust. Using the \"Probability Weighted Moments\" (`method=\"PWM\"`) method can help in that case. This is possible by passing a distribution object from the `lmoments3` package together with `method=\"PWM\"`. That package currently only supports `expon`, `gamma`, `genextreme`, `genpareto`, `gumbel_r`, `pearson3`, and `weibull_min` (with other names, see [the documentation](https://lmoments3.readthedocs.io/en/stable/distributions.html)). In the following example, we fit using the \"Generalized extreme value\" distribution from `lmoments3`."
    ]
   },
   {

xclim/indices/stats.py

@@ -83,7 +83,7 @@ def fit(
         Time series to be fitted along the time dimension.
     dist : str or rv_continuous distribution object
         Name of the univariate distribution, such as beta, expon, genextreme, gamma, gumbel_r, lognorm, norm
-        (see :py:mod:scipy.stats for full list). Or the distribution object itself.
+        (see :py:mod:scipy.stats for full list) or the distribution object itself.
     method : {"ML" or "MLE", "MM", "PWM", "APP"}
         Fitting method, either maximum likelihood (ML or MLE), method of moments (MM) or approximate method (APP).
         Can also be the probability weighted moments (PWM), also called L-Moments, if a compatible `dist` object is passed.