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Version 1.5-2 (CRAN)
Expected Release
Date: 2025-04-01
New Features
We have added three new categorical metrics: Heidke Skill Score
(HSS); Hanssen-Kuipers Discriminant (HK); Equal Threat Score (ETS) or
Gilbert Skill Score.
Aggregate metrics Mean Absolute Error (MAE) within metrics of
goodness of fit.
Since rainfall has a non-parametric behavior, we have modified
the correlation coefficient from Pearson to Spearman. Now, ‘cc’
represents the Spearman correlation coefficient.
We update the “exdata” data to adapt it to the new RFplus
changes.
We have optimized the core of the RFplus algorithm to reduce the
computation time.
When the parameter ‘save_model’ is set to TRUE the message
‘Saving model. Please wait’. To improve readability we have modified
this message to: ‘Model saved successfully’ to indicate that the model
save was successful.
Bug Fixed
- Fixed a bug in the altitude difference calculation, which instead of
calculating altitude differences returned a fixed value.
- An error has been corrected in the learning module where, instead of
extracting the altitude of the station, the altitude of the centroid of
the pixel in which the insitu station was located was extracted.
- Fixed a bug in the calculation of the altitude difference between
the in situ stations and the grid, which was incorrectly performed using
terra::extract(). This method assumed that the altitude of each insitu
station was the altitude of each grid. Now, the difference is correctly
calculated between the altitude of each station and all grid cells.
- Fixed a bug in the examples and in the documentation where the
“Rain_threshold” parameter was passed as a single value and not as a
list, as required by RFplus.
Version 1.4-0 (CRAN)
Expected Release
Date: 2025-03-15
New Features
- The “Description” file has been updated to include all authors who
have contributed to the RFplus package.
- We have made changes to the documentation to ensure better
understanding.
- The evaluation_metrics function has been updated to allow
classification of precipitation data into various intensity categories
(e.g., light, moderate, heavy rain). It now accepts a list of custom
thresholds to define these categories and calculates specific
performance metrics for each, such as Critical Success Index (CSI),
Probability of Detection (POD) and False Alarm Rate (FAR). This
facilitates a more detailed evaluation of model performance at different
rainfall intensities. In addition, the function retains traditional
fitting metrics, such as RMSE and KGE, providing a complete evaluation
tailored to scenarios with rainfall variability.
- Implemented a validation check to identify dates with completely
missing data in BD_insitu. This feature allows users to detect and
visualize dates where all recorded values are NA, preventing the model
from processing them. If such dates are found, the system will trigger a
warning, ensuring data completeness before running the Random Forest
predictions.
- Two additional categorical metrics have been added when “training”
has a value other than 1. The added metrics are: success ratio (SR), Hit
BIAS (HB).
- An update of the vignettes was made to address the improvements
introduced in the previous versions.
Version 1.3-0 (CRAN)
New Features
Removed dependency on ‘dplyr’ and migrated all code to
‘data.table’ to ensure efficiency and speed for large data
sets.
Added functionality to apply point-to-pixel validation. The
metrics analyzed are: Pearson correlation coefficient (CC), root mean
square error (RMSE), modified Kling-Gupta efficiency (KGE), relative
bias (PBIAS), probability of detection (POD), false alarm rate (FAR),
critical success index (CSI).
Removed dependencies on external libraries for FAR, POD, CSI
calculations. Calculations are now performed using R base
functions.
A complete refactoring of the code has been carried out to
improve its efficiency and ease of maintenance.
Bug Fixed
- Fixed a bug in ‘wet.day’ when set to False, rounding was still
performed.
- Fixed ‘pboptions’ slash bug that caused a new slash to be created at
each iteration when setting char = “=”.
Version 1.2-2 (release-CRAN)
Bug Fixed
Version 1.2-1 (release-CRAN)
Bug Fixed
The word quantile mapping was changed to Quantile Mapping due to
CRAN’s comment of “ Words possibly misspelled in DESCRIPTION”.
A test optimization was performed to address the problem of “the
error Executing ‘testthat.R’ [421s/114s] the execution of the R code in
‘testthat.R’ had a CPU time 3.7 times higher than the elapsed time”
reported by CRAN.
Version 1.2-0 (release)
New Features
- The versioning used has been modified. The semantic versioning has
been migrated to the versioning used by CRAN.
- Changed the format for compatibility with the S3 method.
- The input data verification methods were refactored.
- Updated the data for the examples and internal tests
Version 1.1.3
New Features
- Added support for adjusting the simulated distribution by quantile
mapping and nonparametric quantile mapping.
- We have changed the ranger library to Random Forest to make it
compatible with terra predict.
- Improvements have been made to the code to improve
interpretation.
Bug Fixed
- Fixed an ID mess error when transforming the .csv with coordinates
to a vector.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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