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NEWS
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# DownscaleR changelog
See the [Releases section](https://github.com/SantanderMetGroup/downscaleR/releases) for older version changes
## v3.0.0 (14 Jun 2018)
* New user interface for flexible definition of predictors (`prepareData`) and prediction data (`prepareNewData`).
* New method (neural networks) available for perfect-prog downscaling
* New workhorse function `downscale` for perfect-prog/MOS downscaling with different methods (GLM, Analogs..., more soon)
* Flexible method calibration/prediction via `downscale.train` and `downscale.predict`
* New helper function for flexible cross-validation experimental setups: `downscale.cv`
* Improved efficiency in `biasCorrection`, including a parallelization option
* New options for a more flexible parametric quantile mapping method design
* All changes in predictor/predictand definition have been double-checked against MeteoLab for consistency
* Other documentation updates and enhancements
* See the updates in the [wiki](https://github.com/SantanderMetGroup/downscaleR/wiki) for worked examples
## v3.0.1 (21 Jun 2018)
* Improved `downscale.cv` input arguments, and its documentation. Added new option for leave-one-year-out cross validation
* Other documentation updates
## v3.0.2 (30 Jul 2018)
* Replaced calls to bindGrid.time by bindGrid (the former not exported anymore)
## v3.0.3 (30 Aug 2018)
* NAs are now removed from observation data series when using the scaling method for biasCorrection.
## v3.0.4 (23 Sep 2018)
* Add new CITATION
* Edited startup package message
## v3.0.5 (17 Dic 2018)
* Add important explanation in the biasCorrection documentation
* Bug fix in argument which.combine of prepareData
* Adapt biasCorrection to grids with a single dimension (time)
* NA handling in precipitation frequency adjustment in biasCorrection
* Code improvement
## v3.0.6 (16 Apr 2019)
* downscale.cv admits regular grids
* New method for bias correction: mva (Mean and Variance Adjustment)
* Other minor changes and documentation updates
## v3.1.0 (09 Jul 2019)
* New `downscaleChunk` function, to handle large experiments by chunks
* downscale.cv renamed --> downscaleCV
* downscale.train renamed --> downscaleTrain
* downscale.predict renamed --> downscalePredict
* Other minor changes and documentation updates
## v3.1.1 (5 Feb 2020)
* Bug fix in `downscaleCV'
* Improve the internal code of `downscaleChunk'
* Add qdm and dqm bias correction methods
* Bug fix in `biasCorrection'
## v3.1.2 (12 Mar 2020)
* Bug fix in `isimip' (wet-day frequency correction)
* Bug fix in downscaleTrain -- NaNs in single-site
## v3.1.3 (24 Mar 2020)
* New dependency: 'library sticky' for attributes containing variable information
## v3.2.0 (23 Apr 2020)
* New ISIMIP3 routine for bias correction
* Internal python routines
* Other documentation updates (CITATION, etc.)
## v3.3.0 (23 May 2020)
* Update examples and other minor updates to include the new datasets from climate4R.datasets
## v3.3.1 (04 Jun 2020)
* New parameter in downscaleTrain: 'predict', to return the prediction in the train set
* Internal flag to force non-overlapping nearest-neighbour application within `biasCorrection` (recommended for chunking, depends on transformeR >= 2.0.1)
* Avoid dependency from lubridate
* Other minor changes and documentation updates
## v3.3.2 (09 Feb 2021)
* Minor internal changes in biasCorrection for improved memory usage
* Other minor changes and documentation updates
* New optional parameter in functions downscalePredict, downscaleTrain, donwscaleCV and downscale called `simulate` that permits to simulate from the distributional parameters infered from the GLMs
## v3.3.3 (05 Jul 2021)
* Minor bug fixes, documentation updates and enhancements
## V3.3.4 (23 Jun 2023)
* Import fillGrid instead of fillGridDates
* Add LICENSE file