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This package has been conceived to work in the framework of both seasonal forecasting and climate change studies. Thus, it considers ensemble members as a basic dimension of the data structure. Find out more about this package at the [downscaleR wiki](https://github.com/SantanderMetGroup/downscaleR/wiki). \n\nThis package is part of the [climate4R bundle](http://www.meteo.unican.es/climate4r), formed by `loadeR`, `transformeR`, `downscaleR` and `visualizeR`. The recommended installation procedure is to use the `install_github` command from the devtools R package:\n\n```r\ndevtools::install_github(c(\"SantanderMetGroup/transformeR\", \"SantanderMetGroup/downscaleR\"))\n```\n**NOTE:** Note that `transformeR` is a dependency for `downscaleR`. The utilities in `transformeR` were formerly part of `downscaleR` (up to v1.3-4). Since `downscaleR` v2.0-0, these are in `transformeR` and `downscaleR` is strictly aimed to statistical downscaling. Note that `transformeR` also includes illustrative datasets for the `climate4r`framework.\n\n**EXAMPLE:** The following code trains three different downscaling methods (analogs, linear regression and neural networks) using principal components (explaining 95\\% of the variance for each variable) and visualizes the results (the illustrative station and reanalysis data for DJF included in the `transformeR` package is used in this example): \n```r\nlibrary(downscaleR)\ndata(\"VALUE_Iberia_tas\") # illustrative datasets included in transformeR\ny \u003c- VALUE_Iberia_tas \ndata(\"NCEP_Iberia_hus850\", \"NCEP_Iberia_psl\", \"NCEP_Iberia_ta850\")\nx \u003c- makeMultiGrid(NCEP_Iberia_hus850, NCEP_Iberia_psl, NCEP_Iberia_ta850)\n# calculating predictors\ndata \u003c- prepareData(x = x, y = y,spatial.predictors = list(v.exp = 0.95)) \n# Fitting statistical downscaling methods (simple case, no cross-validation)\nanalog \u003c- downscale.train(data, method = \"analogs\", n.analogs = 1)\nregression \u003c- downscale.train(data, method = \"GLM\",family = gaussian)\nneuralnet \u003c- downscale.train(data, method = \"NN\", hidden = c(10,5), output = \"linear\")\n# Extracting the results for a particula station (Igueldo) for a single year (2000)\nigueldo.2000 \u003c- subsetGrid(y,station.id = \"000234\",years = 2000)\nanalog.2000 \u003c- subsetGrid(analog$pred,station.id = \"000234\",years = 2000)\nregression.2000 \u003c- subsetGrid(regression$pred,station.id = \"000234\",years = 2000)\nneuralnet.2000 \u003c- subsetGrid(neuralnet$pred,station.id = \"000234\",years = 2000)\nlibrary(visualizeR)  # Data visualization utilities\ntemporalPlot(igueldo.2000, analog.2000, regression.2000, neuralnet.2000)\n```\n\n---\nReference and further information: \n\n**[General description of the downscaleR package]** Bedia et al. 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