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EMAR
View on CRAN: Click
here
Download and install EMAR package within the R console
Install from CRAN:
install.packages("EMAR")
Install from Github:
library("remotes")
install_github("cran/EMAR")
Install by package version:
library("remotes")
install_version("EMAR", "1.0.0")
Attach the package and use:
library("EMAR")
Maintained by
Friday Nwabueze Ogana
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-11-10
Latest Update: 2023-11-10
Description:
A tool that allows users to generate various indices for evaluating statistical models. The fitstat() function computes indices based on the fitting data. The valstat() function computes indices based on the validation data set. Both fitstat() and valstat() will return 16 indices SSR: residual sum of squares, TRE: total relative error, Bias: mean bias, MRB: mean relative bias, MAB: mean absolute bias, MAPE: mean absolute percentage error, MSE: mean squared error, RMSE: root mean square error, Percent.RMSE: percentage root mean squared error, R2: coefficient of determination, R2adj: adjusted coefficient of determination, APC: Amemiya's prediction criterion, logL: Log-likelihood, AIC: Akaike information criterion, AICc: corrected Akaike information criterion, BIC: Bayesian information criterion, HQC: Hannan-Quin information criterion. The lower the better for the SSR, TRE, Bias, MRB, MAB, MAPE, MSE, RMSE, Percent.RMSE, APC, AIC, AICc, BIC and HQC indices. The higher the better for R2 and R2adj indices. Petre Stoica, P., Sel
How to cite:
Friday Nwabueze Ogana (2023). EMAR: Empirical Model Assessment. R package version 1.0.0, https://cran.r-project.org/web/packages/EMAR
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