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metrica
View on CRAN: Click
here
Download and install metrica package within the R console
Install from CRAN:
install.packages("metrica")
Install from Github:
library("remotes")
install_github("cran/metrica")
Install by package version:
library("remotes")
install_version("metrica", "2.1.0")
Attach the package and use:
library("metrica")
Maintained by
Adrian A. Correndo
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-12
Latest Update: 2023-04-14
Description:
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 .
How to cite:
Adrian A. Correndo (2022). metrica: Prediction Performance Metrics. R package version 2.1.0, https://cran.r-project.org/web/packages/metrica. Accessed 21 Nov. 2024.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for metrica
Functions, R codes and Examples using
the metrica R package
Some associated functions: AC . AUC_roc . B0_sma . B1_sma . CCC . E1 . Erel . KGE . LCS . MAE . MAPE . MASE . MBE . MIC . MLA . MLP . MSE . NSE . PAB . PBE . PLA . PLP . PPB . R2 . RAC . RAE . RMAE . RMLA . RMLP . RMSE . RRMSE . RSE . RSR . RSS . SB . SDSD . SMAPE . TSS . Ub . Uc . Ue . Xa . accuracy . agf . balacc . barley . bland_altman_plot . bmi . chickpea . confusion_matrix . csi . d . d1 . d1r . dcorr . deltap . density_plot . error_rate . fmi . fscore . gmean . import_apsim_db . import_apsim_out . iqRMSE . khat . lambda . land_cover . likelihood_ratios . maize_phenology . mcc . metrica-package . metrics_summary . npv . precision . prevalence . r . recall . scatter_plot . sorghum . specificity . tiles_plot . uSD . var_u . wheat .
Some associated R codes: class_AUC_roc.R . class_accuracy.R . class_agf.R . class_balacc.R . class_bmi.R . class_confusion_matrix.R . class_csi.R . class_deltap.R . class_error_rate.R . class_fmi.R . class_fscore.R . class_gmean.R . class_khat.R . class_likelihood_ratios.R . class_mcc.R . class_npv.R . class_precision.R . class_prevalence.R . class_recall.R . class_specificity.R . data.R . import_apsim_db.R . import_apsim_out.R . metrica-package.R . metrics_summary.R . plot_bland_altman.R . plot_density.R . plot_scatter.R . plot_tiles.R . reg_AC.R . reg_B0_sma.R . reg_B1_sma.R . reg_CCC.R . reg_E1.R . reg_Erel.R . reg_KGE.R . reg_LCS.R . reg_MAE.R . reg_MAPE.R . reg_MASE.R . reg_MBE.R . reg_MIC.R . reg_MLA.R . reg_MLP.R . reg_MSE.R . reg_NSE.R . reg_PAB.R . reg_PBE.R . reg_PLA.R . reg_PLP.R . reg_PPB.R . reg_R2.R . reg_RAC.R . reg_RAE.R . reg_RMAE.R . reg_RMLA.R . reg_RMLP.R . reg_RMSE.R . reg_RRMSE.R . reg_RSE.R . reg_RSR.R . reg_RSS.R . reg_SB.R . reg_SDSD.R . reg_SMAPE.R . reg_TSS.R . reg_Ub.R . reg_Uc.R . reg_Ue.R . reg_Xa.R . reg_d.R . reg_d1.R . reg_d1r.R . reg_dcorr.R . reg_iqRMSE.R . reg_lambda.R . reg_r.R . reg_uSD.R . reg_var_u.R . Full metrica package functions and examples
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