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autoann  

Neural Network–Based Model Selection and Forecasting
View on CRAN: Click here


Download and install autoann package within the R console
Install from CRAN:
install.packages("autoann")

Install from Github:
library("remotes")
install_github("cran/autoann")

Install by package version:
library("remotes")
install_version("autoann", "0.1.0")



Attach the package and use:
library("autoann")
Maintained by
Dr. Pramit Pandit
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-01-15
Latest Update: 2026-01-15
Description:
Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.
How to cite:
Dr. Pramit Pandit (2026). autoann: Neural Network–Based Model Selection and Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/autoann. Accessed 04 Jul. 2026.
Previous versions and publish date:
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Complete documentation for autoann
Functions, R codes and Examples using the autoann R package
Full autoann package functions and examples
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