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.
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.
We modified the description of the package to meet the corrections suggested by CRAN.
Replaced by \dontrun with \donttest. due to the time of execution of the example. (> 5 seconds)
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.