update install instructions + paper title

doc/source/index.rst

@@ -18,16 +18,24 @@ S+LEAF extends the celerite model in two ways:
   with the same Gaussian processes and their derivatives
   (see [4]_ for more details).
 
+Please cite [2]_ and [4]_ if you use S+LEAF in a publication.
+
 Installation
 ------------
 
-The S+LEAF package can be installed using the pip utility
+Using conda
+~~~~~~~~~~~
 
-``pip install spleaf``
+The S+LEAF package can be installed using conda with the following command:
+
+``conda install -c conda-forge spleaf``
 
-and upgraded with
+Using pip
+~~~~~~~~~
 
-``pip install spleaf --upgrade``
+It can also be installed using pip with:
+
+``pip install spleaf``
 
 Usage
 -----
@@ -80,4 +88,4 @@ References
 .. [1] `Foreman-Mackey et al., "Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series", 2017 <http://adsabs.harvard.edu/abs/2017AJ....154..220F>`_.
 .. [2] `Delisle, J.-B., Hara, N., and Ségransan, D., "Efficient modeling of correlated noise. II. A flexible noise model with fast and scalable methods", 2020 <https://ui.adsabs.harvard.edu/abs/2020A\&A...638A..95D>`_.
 .. [3] `Gordon, T. A., Agol, E., Foreman-Mackey, D., "A Fast, Two-dimensional Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization", 2020 <https://ui.adsabs.harvard.edu/abs/2020AJ....160..240G>`_.
-.. [4] Delisle, J.-B., Unger, N., Hara, N., and Ségransan, D., "Efficient modeling of correlated noise. III. Fast and scalable Gaussian processes over heterogeneous time series", submitted to A&A.
+.. [4] Delisle, J.-B., Unger, N., Hara, N., and Ségransan, D., "Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes", submitted to A&A.