Merge branch 'master' of https://github.com/MNLR/RandomForestDist

Author: MNLR

.gitattributes

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 WorkedExample.ipynb linguist-detectable=false
+WorkedExample.html linguist-detectable=false

README.md

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 ## RandomForestDist
 
-Building on a modified version of [rpart](https://cran.r-project.org/web/packages/rpart/index.html) which can be found [here](https://github.com/MNLR/rpart), this package implements advanced functionalities for random forests [(Breiman, L. Random Forests. Machine Learning 45, 5–32, 2001)](https://doi.org/10.1023/A:1010933404324) which make this technique suitable for statistical downscaling of precipitation, as analyzed in *Legasa et al. 2021* (submitted to *Water Resources Research*). The key elements of [RandomForestDist](https://github.com/MNLR/RandomForestDist) are:
+Building on a modified version of [rpart](https://cran.r-project.org/web/packages/rpart/index.html) which can be found [here](https://github.com/MNLR/rpart), this package implements advanced functionalities for random forests [(Breiman, L. Random Forests. Machine Learning 45, 5–32, 2001)](https://doi.org/10.1023/A:1010933404324) which make this technique suitable for statistical downscaling of precipitation, as analyzed in [**Legasa et al. 2022: A Posteriori Random Forests for Stochastic Downscaling of Precipitation by Predicting Probability Distributions**](https://doi.org/10.1029/2021WR030272), published in *Water Resources Research*. The key elements of [RandomForestDist](https://github.com/MNLR/RandomForestDist) are:
 
 * The inclussion of several split functions intended for predictand variables that are non-normally distributed. In *Legasa et al. 2022* , we focus on the two-parameter gamma distribution (Deviation and Log Likelihood). However, other distributions can be easily added through the [modified rpart package](https://github.com/MNLR/rpart).
 

WorkedExample.ipynb

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    "source": [
     "# Brief User Guide for the [**RandomForestDist**](https://github.com/MNLR/RandomForestDist) Package \n",
     "\n",
-    "This companion notebook briefly explains how to use the main functionalities provided by the `R` package [RandomForestDist](https://github.com/MNLR/RandomForestDist) and presents the code needed to reproduce part of the experiments presented in the article *A Posteriori Random Forests for Stochastic\n",
-    "Downscaling of Precipitation by Predicting Probability Distributions*, submitted to *Water Resources Research* by *Legasa et al.* in 2022. In that work, random forests (RFs) are applied to the problem of statistical downscaling of rainfall. In particular, the authors analyze the suitability of different split functions which allow to work with non-normally distributed variables and propose a novel a posteriori (AP) approach which permits to accurately estimate the shape and rate parameters of the underlying rainfall distribution, which in turn allows for generating reliable stochastic rainfall series. \n",
+    "This companion notebook briefly explains how to use the main functionalities provided by the `R` package [RandomForestDist](https://github.com/MNLR/RandomForestDist) and presents the code needed to reproduce part of the experiments presented in the article [**A Posteriori Random Forests for Stochastic\n",
+    "Downscaling of Precipitation by Predicting Probability Distributions**](https://doi.org/10.1029/2021WR030272), published in *Water Resources Research* by *Legasa et al.* in 2022. In that work, random forests (RFs) are applied to the problem of statistical downscaling of rainfall. In particular, the authors analyze the suitability of different split functions which allow to work with non-normally distributed variables and propose a novel a posteriori (AP) approach which permits to accurately estimate the shape and rate parameters of the underlying rainfall distribution, which in turn allows for generating reliable stochastic rainfall series. \n",
     "\n",
     "[RandomForestDist](https://github.com/MNLR/RandomForestDist) requires a modified version of [rpart](https://cran.r-project.org/web/packages/rpart/index.html) which can be found [here](https://github.com/MNLR/rpart). In order to run the examples provided below, these two packages need to be first installed. This can be easily done from GitHub using the `devtools` package: "
    ]