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@@ -11,7 +11,7 @@ quantile-forest
 
    **quantile-forest** is an implementation of scikit-learn compatible quantile regression forests.
 
-   Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles, as described by :cite:t:`2006:meinshausen`. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn variants.
+   Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles, as described by :cite:t:`2006:meinshausen`. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn forest regressors.
 
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@@ -385,7 +385,7 @@
 <h1>quantile-forest<a class="headerlink" href="#quantile-forest" title="Link to this heading">#</a></h1>
 <p><strong>Version</strong>: 1.3.9</p>
 <p class="lead"><strong>quantile-forest</strong> is an implementation of scikit-learn compatible quantile regression forests.</p>
-<p class="lead">Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles, as described by <span id="id1">Meinshausen [<a class="reference internal" href="references.html#id3" title="Nicolai Meinshausen. Quantile Regression Forests. Journal of Machine Learning Research, 7(6), 983-999, 2006. URL: https://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf.">Mei06</a>]</span>. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn variants.</p>
+<p class="lead">Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles, as described by <span id="id1">Meinshausen [<a class="reference internal" href="references.html#id3" title="Nicolai Meinshausen. Quantile Regression Forests. Journal of Machine Learning Research, 7(6), 983-999, 2006. URL: https://www.jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf.">Mei06</a>]</span>. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn forest regressors.</p>
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