From 778350e74d498ececdc965f04169d9cd1fbbd10a Mon Sep 17 00:00:00 2001
From: reidjohnson <reidjohnson@users.noreply.github.com>
Date: Thu, 29 Aug 2024 06:39:04 +0000
Subject: [PATCH] deploy: 666dd81a3d896d7285d57630ad62d0e9bd1b9fa7

---
 _sources/index.rst.txt | 2 +-
 index.html             | 2 +-
 searchindex.js         | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index 5170d93..069839b 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -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.
 
 .. grid:: 1 1 2 2
    :padding: 0 2 3 5
diff --git a/index.html b/index.html
index efe4334..fedd034 100644
--- a/index.html
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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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diff --git a/searchindex.js b/searchindex.js
index 08e610f..fa1f261 100644
--- a/searchindex.js
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@@ -1 +1 @@
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