Prediction Performance Metrics // A compilation of more than 80
functions designed to quantitatively and visually evaluate
prediction performance of regression (continuous variables) and
classification (categorical variables) of point-forecast models
(e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For
regression, it includes functions to generate plots (scatter,
tiles, density, Bland-Altman plot), and to estimate error
metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack
of accuracy-precision), model efficiency (e.g. NSE, E1, KGE),
indices of agreement (e.g. d, RAC), goodness of fit (e.g. r,
R2), adjusted correlation coefficients (e.g. CCC, dcorr),
symmetric regression coefficients (intercept, slope), and mean
absolute scaled error (MASE) for time series predictions. For
classification (binomial and multinomial), it offers functions
to generate and plot confusion matrices, and to estimate
performance metrics such as accuracy, precision, recall,
specificity, F-score, Cohen's Kappa, G-mean, and many more. For
more details visit the vignettes
https://adriancorrendo.github.io/metrica/.