AllMetrics: Calculating Multiple Performance Metrics of a Prediction Model

Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Version:	0.2.1
Published:	2024-03-12
Author:	Dr. Sandip Garai [aut, cre]
Maintainer:	Dr. Sandip Garai <sandipnicksandy at gmail.com>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	AllMetrics results

Documentation:

Reference manual:

AllMetrics.pdf

Downloads:

Package source:	AllMetrics_0.2.1.tar.gz
Windows binaries:	r-devel: AllMetrics_0.2.1.zip, r-release: AllMetrics_0.2.1.zip, r-oldrel: AllMetrics_0.2.1.zip
macOS binaries:	r-release (arm64): AllMetrics_0.2.1.tgz, r-oldrel (arm64): AllMetrics_0.2.1.tgz, r-release (x86_64): AllMetrics_0.2.1.tgz, r-oldrel (x86_64): AllMetrics_0.2.1.tgz
Old sources:	AllMetrics archive

Reverse dependencies:

Reverse imports:

AriGaMyANNSVR, tsLSTMx

Linking:

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