minors for joss publication

paper/paper.bib

@@ -69,7 +69,7 @@ @article{scheffer_early-warning_2009
 }
 
 @article{bury_ewstools_2023,
-	title = {ewstools: {A} {Python} package for early warning signals ofbifurcations in time series data},
+	title = {ewstools: {A} {Python} package for early warning signals of bifurcations in time series data},
 	volume = {8},
 	issn = {2475-9066},
 	shorttitle = {ewstools},

paper/paper.md

@@ -45,7 +45,7 @@ and 2095 has led to no less than 870 news outlets and 4100 tweets [@ditlevsen_wa
 largely because of the substantial implications of such a collapse for human societies.
 A common concern in the scientific community is that published work on the topic is difficult
 to reproduce, despite the impact it implies for humanity. Additionally, existing softwares
-[@dakos_methods_2012, @bury_ewstools_2023] are well suited to apply some state-of-the-art
+[@dakos_methods_2012,@bury_ewstools_2023] are well suited to apply some state-of-the-art
 techniques but lack the possibility of being easily extended by the users, thus making them
 unsuited for some research task.
 Both of these issues can be largely addressed by a unifying software that is accessible,
@@ -106,12 +106,12 @@ fig = plot_changes_significance(results, signif)
 We apply this code to data generated by a Ricker model presenting an abrupt transition
 at $t = 860$, which is used in the first tutorial of `ewstools` [@bury_ewstools_2023],
 the most recent software covering similar functionalities.
-The results are shown in [Fig. 1](@figure1) and display, as expected from CSD theory, an
+The results are shown in \autoref{fig:figure1} and display, as expected from CSD theory, an
 increase in both variance and AR1 coefficient, which is exactly the same as computed
 by `ewstools`. However, calling `signif.pvalues` shows that the increase in variance is not
 significant ($p = 0.284$), whereas the increase in AR1 coefficient is ($p = 0.001$).
 
-![Output of plotting function in usage example.\label{fig: fig1}](figures/figure1.png)
+![Output of plotting function in usage example.\label{fig:figure1}](figures/figure1.png)
 
 We believe that a concise and unambiguous code will greatly reduce the programming effort of
 many researchers and ease the code reviewing process. Finally, the code
@@ -228,13 +228,13 @@ TransitionsInTimeseries.jl.
 
 Using TransitionsInTimeseries.jl, we reproduced the computations showcased in Tutorial 1
 and Tutorial 2 of the `ewstools` (v2.1.0) documentation, along with the block bootstrapping.
-The runtimes of both softwares are benchmarked in [Fig. 2](@figure2).
+The runtimes of both softwares are benchmarked in \autoref{fig:figure2}.
 It appears that most computations are faster in TransitionsInTimeseries, with a speed-up
 factor ranging from 0 to 3.5 orders of magnitude. The implementation of the deep-learning
-classifiers for transition prediction developed in [@bury_deep_2021], as well as dealing with
+classifiers for transition prediction developed in @bury_deep_2021, as well as dealing with
 multidimensional timeseries, are part of future developments of TransitionsInTimeseries.jl.
 
-![Performance comparison between `ewstools` and TransitionsInTimeseries.jl.\label{fig: fig2}](figures/figure2.png)
+![Performance comparison between `ewstools` and TransitionsInTimeseries.jl.\label{fig:figure2}](figures/figure2.png)
 
 # Documentation