Package: ModelMetrics 1.2.3

ModelMetrics: Rapid Calculation of Model Metrics

Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.

Authors:Tyler Hunt [aut, cre]

ModelMetrics_1.2.3.tar.gz
ModelMetrics_1.2.3.zip(r-4.5)ModelMetrics_1.2.3.zip(r-4.4)ModelMetrics_1.2.3.zip(r-4.3)
ModelMetrics_1.2.3.tgz(r-4.4-x86_64)ModelMetrics_1.2.3.tgz(r-4.4-arm64)ModelMetrics_1.2.3.tgz(r-4.3-x86_64)ModelMetrics_1.2.3.tgz(r-4.3-arm64)
ModelMetrics_1.2.3.tar.gz(r-4.5-noble)ModelMetrics_1.2.3.tar.gz(r-4.4-noble)
ModelMetrics.pdf |ModelMetrics.html✨
ModelMetrics/json (API)
NEWS

# Install 'ModelMetrics' in R:

install.packages('ModelMetrics', repos = c('https://jackstat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jackstat/modelmetrics/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

testDF - Test data

On CRAN:

auc logloss machine-learning metrics model-evaluation model-metrics

11.83 score 29 stars 291 packages 1.3k scripts 106k downloads 2 mentions 25 exports 2 dependencies

Last updated 3 years agofrom:3da8b8452d. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 08 2024
R-4.5-win-x86_64	OK	Nov 08 2024
R-4.5-linux-x86_64	OK	Nov 08 2024
R-4.4-win-x86_64	OK	Nov 08 2024
R-4.4-mac-x86_64	OK	Nov 08 2024
R-4.4-mac-aarch64	OK	Nov 08 2024
R-4.3-win-x86_64	OK	Nov 08 2024
R-4.3-mac-x86_64	OK	Nov 08 2024
R-4.3-mac-aarch64	OK	Nov 08 2024

Exports:auc brier ce confusionMatrix f1Score fScore gini kappa logLoss mae mauc mcc mlogLoss mse msle npv ppv precision recall rmse rmsle sensitivity specificity tnr tpr

Dependencies:data.table Rcpp

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Area Under the Curve	auc auc.default auc.gbm auc.glm auc.glmerMod auc.randomForest auc.rpart
Brier Score	brier brier.default brier.gbm brier.glm brier.glmerMod brier.randomForest brier.rpart
Classification error	ce ce.default ce.gbm ce.glm ce.glmerMod ce.lm ce.randomForest ce.rpart
Confusion Matrix	confusionMatrix
F1 Score	f1Score
F Score	fScore
GINI Coefficient	gini gini.default gini.gbm gini.glm gini.glmerMod gini.randomForest gini.rpart
kappa statistic	kappa
Log Loss	logLoss logLoss.default logLoss.gbm logLoss.glm logLoss.glmerMod logLoss.randomForest logLoss.rpart
Mean absolute error	mae mae.default mae.gbm mae.glm mae.glmerMod mae.randomForest mae.rpart
Multiclass Area Under the Curve	mauc
Matthews Correlation Coefficient	mcc
Multiclass Log Loss	mlogLoss
Mean Square Error	mse mse.default mse.glm mse.lm
Mean Squared Log Error	msle msle.default msle.gbm msle.glm msle.glmerMod msle.lm msle.randomForest msle.rpart
Negative Predictive Value	npv
Positive Predictive Value	ppv precision
Recall, Sensitivity, tpr	recall sensitivity tpr
Root-Mean Square Error	rmse rmse.default rmse.glm rmse.lm
Root Mean Squared Log Error	rmsle rmsle.default rmsle.gbm rmsle.glm rmsle.glmerMod rmsle.lm rmsle.randomForest rmsle.rpart
Test data	testDF
Specificity, True negative rate	specificity tnr