-Collin, François-David, Ghislain Durif, Louis Raynal, Eric Lombaert,
-Mathieu Gautier, Renaud Vitalis, Jean-Michel Marin, and Arnaud Estoup.
-2021. “Extending Approximate Bayesian Computation with Supervised
-Machine Learning to Infer Demographic History from Genetic Polymorphisms
-Using DIYABC Random Forest.” *Molecular Ecology Resources* 21 (8):
-2598–2613. https://doi.org/.
+Collin, François-David, Ghislain Durif, Louis Raynal, Eric Lombaert, Mathieu Gautier, Renaud Vitalis, Jean-Michel Marin, and Arnaud Estoup. 2021. “Extending Approximate Bayesian Computation with Supervised Machine Learning to Infer Demographic History from Genetic Polymorphisms Using DIYABC Random Forest.” *Molecular Ecology Resources* 21 (8): 2598–2613. https://doi.org/.
-Collin, François-David, Arnaud Estoup, Jean-Michel Marin, and Louis
-Raynal. 2020. “Bringing ABC inference to the
-machine learning realm : AbcRanger, an optimized random forests library
-for ABC.” In *JOBIM 2020*, 2020:66. JOBIM. Montpellier, France.
-.
+Collin, François-David, Arnaud Estoup, Jean-Michel Marin, and Louis Raynal. 2020. “Bringing ABC inference to the machine learning realm : AbcRanger, an optimized random forests library for ABC.” In *JOBIM 2020*, 2020:66. JOBIM. Montpellier, France. .
-Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. *The
-Elements of Statistical Learning*. Vol. 1. 10. Springer series in
-statistics New York, NY, USA:
+Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. *The Elements of Statistical Learning*. Vol. 1. 10. Springer series in statistics New York, NY, USA:
-Guennebaud, Gaël, Benoît Jacob, et al. 2010. “Eigen V3.”
-http://eigen.tuxfamily.org.
+Guennebaud, Gaël, Benoît Jacob, et al. 2010. “Eigen V3.” http://eigen.tuxfamily.org.
-Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014.
-“Mondrian Forests: Efficient Online Random Forests.” In *Advances in
-Neural Information Processing Systems*, 3140–48.
+Lakshminarayanan, Balaji, Daniel M Roy, and Yee Whye Teh. 2014. “Mondrian Forests: Efficient Online Random Forests.” In *Advances in Neural Information Processing Systems*, 3140–48.
-Lintusaari, Jarno, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén,
-Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka
-Corander, and Samuel Kaski. 2018. “ELFI: Engine for Likelihood-Free
-Inference.” *Journal of Machine Learning Research* 19 (16): 1–7.
-.
+Lintusaari, Jarno, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, and Samuel Kaski. 2018. “ELFI: Engine for Likelihood-Free Inference.” *Journal of Machine Learning Research* 19 (16): 1–7. .
-Pudlo, Pierre, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet,
-Mathieu Gautier, and Christian P Robert. 2015. “Reliable ABC Model
-Choice via Random Forests.” *Bioinformatics* 32 (6): 859–66.
+Pudlo, Pierre, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet, Mathieu Gautier, and Christian P Robert. 2015. “Reliable ABC Model Choice via Random Forests.” *Bioinformatics* 32 (6): 859–66.
-Raynal, Louis, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet,
-Christian P Robert, and Arnaud Estoup. 2018. “ABC
-random forests for Bayesian parameter inference.”
-*Bioinformatics* 35 (10): 1720–28.
-.
+Raynal, Louis, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P Robert, and Arnaud Estoup. 2018. “ABC random forests for Bayesian parameter inference.” *Bioinformatics* 35 (10): 1720–28. .
-Wright, Marvin N, and Andreas Ziegler. 2015. “Ranger: A Fast
-Implementation of Random Forests for High Dimensional Data in c++ and
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+Wright, Marvin N, and Andreas Ziegler. 2015. “Ranger: A Fast Implementation of Random Forests for High Dimensional Data in c++ and r.” *arXiv Preprint arXiv:1508.04409*.
-[^1]: The term “online” there and in the code has not the usual meaning
- it has, as coined in “online machine learning”. We still need the
- entire training data set at once. Our implementation is an “online”
- one not by the sequential order of the input data, but by the
- sequential order of computation of the trees in random forests,
- sequentially computed and then discarded.
+[^1]: The term “online” there and in the code has not the usual meaning it has, as coined in “online machine learning”. We still need the entire training data set at once. Our implementation is an “online” one not by the sequential order of the input data, but by the sequential order of computation of the trees in random forests, sequentially computed and then discarded.
-[^2]: We only use the C++ Core of ranger, which is under [MIT
- License](https://raw.githubusercontent.com/imbs-hl/ranger/master/cpp_version/COPYING),
- same as ours.
+[^2]: We only use the C++ Core of ranger, which is under [MIT License](https://raw.githubusercontent.com/imbs-hl/ranger/master/cpp_version/COPYING), same as ours.
diff --git a/README.qmd b/README.qmd
index 1e5b95c..54e4f2b 100644
--- a/README.qmd
+++ b/README.qmd
@@ -5,10 +5,8 @@ bibliography: [ref.bib]
toc: true
toc-depth: 1
format:
- gfm:
- html-math-method:
- method: webtex
- url: https://latex.codecogs.com/png.image?%5Cbg_black&space;
+ gfm:
+ wrap: none
---
@@ -229,3 +227,5 @@ where $\widetilde{\mathbf{P}}_{K \times p}=\mathbf{t}\left[p_{1}, \ldots, p_{K}\
This have been the subject of a proceedings in [JOBIM 2020](https://jobim2020.sciencesconf.org/), [PDF](https://hal.archives-ouvertes.fr/hal-02910067v2) and [video](https://relaiswebcasting.mediasite.com/mediasite/Play/8ddb4e40fc88422481f1494cf6af2bb71d?catalog=e534823f0c954836bf85bfa80af2290921) (in french), [@collin:hal-02910067].
+::: {.references}
+:::
\ No newline at end of file
+
+
+
+