The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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.
| Version: | 0.1.0 |
| Imports: | nnet |
| Published: | 2026-01-15 |
| DOI: | 10.32614/CRAN.package.autoann (may not be active yet) |
| Author: | Dr. Pramit Pandit [aut, cre], Ms. Moumita Paul [aut], Dr. Bikramjeet Ghose [aut] |
| Maintainer: | Dr. Pramit Pandit <pramitpandit at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | autoann results |
| Reference manual: | autoann.html , autoann.pdf |
| Package source: | autoann_0.1.0.tar.gz |
| Windows binaries: | r-devel: autoann_0.1.0.zip, r-release: not available, r-oldrel: autoann_0.1.0.zip |
| macOS binaries: | r-release (arm64): autoann_0.1.0.tgz, r-oldrel (arm64): autoann_0.1.0.tgz, r-release (x86_64): autoann_0.1.0.tgz, r-oldrel (x86_64): autoann_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=autoann to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.