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RSNNS  

Neural Networks using the Stuttgart Neural Network Simulator (SNNS)
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("RSNNS", "0.4-17")



Attach the package and use:
library("RSNNS")
Maintained by
Christoph Bergmeir
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2010-11-15
Latest Update: 2023-11-30
Description:
The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.
How to cite:
Christoph Bergmeir (2010). RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS). R package version 0.4-17, https://cran.r-project.org/web/packages/RSNNS. Accessed 15 Jul. 2026.
Previous versions and publish date:
(2026-07-09 08:20), 0.3-1 (2010-11-17 17:20), 0.3 (2010-11-15 14:14), 0.4-0 (2011-06-21 15:03), 0.4-1 (2011-07-28 19:53), 0.4-2 (2011-09-30 07:51), 0.4-3 (2012-01-10 14:37), 0.4-4 (2013-12-16 23:00), 0.4-5 (2014-05-23 09:02), 0.4-6 (2014-12-22 06:27), 0.4-7 (2015-06-12 10:49), 0.4-9 (2016-12-16 08:33), 0.4-10.1 (2018-08-10 08:46), 0.4-10 (2017-12-10 13:37), 0.4-11 (2018-08-10 23:50), 0.4-12 (2019-09-17 06:40), 0.4-13 (2021-08-09 06:30), 0.4-14 (2021-08-13 12:10), 0.4-15 (2023-01-23 15:50), 0.4-16 (2023-05-09 09:40), 0.4-17 (2023-11-30 06:50)
Other packages that cited RSNNS R package
View RSNNS citation profile
Other R packages that RSNNS depends, imports, suggests or enhances
Complete documentation for RSNNS
Functions, R codes and Examples using the RSNNS R package
Some associated functions: RSNNS-package . SnnsR-class . SnnsRObject-createNet . SnnsRObject-createPatSet . SnnsRObject-extractNetInfo . SnnsRObject-extractPatterns . SnnsRObject-getAllHiddenUnits . SnnsRObject-getAllInputUnits . SnnsRObject-getAllOutputUnits . SnnsRObject-getAllUnits . SnnsRObject-getAllUnitsTType . SnnsRObject-getCompleteWeightMatrix . SnnsRObject-getInfoHeader . SnnsRObject-getSiteDefinitions . SnnsRObject-getTypeDefinitions . SnnsRObject-getUnitDefinitions . SnnsRObject-getUnitsByName . SnnsRObject-getWeightMatrix . SnnsRObject-initializeNet . SnnsRObject-predictCurrPatSet . SnnsRObject-resetRSNNS . SnnsRObject-setTTypeUnitsActFunc . SnnsRObject-setUnitDefaults . SnnsRObject-somPredictComponentMaps . SnnsRObject-somPredictCurrPatSetWinners . SnnsRObject-somPredictCurrPatSetWinnersSpanTree . SnnsRObject-train . SnnsRObject-whereAreResults . SnnsRObjectFactory . SnnsRObjectMethodCaller . analyzeClassification . art1 . art2 . artmap . assoz . confusionMatrix . decodeClassLabels . denormalizeData . dlvq . elman . encodeClassLabels . exportToSnnsNetFile . extractNetInfo . getNormParameters . getSnnsRDefine . getSnnsRFunctionTable . inputColumns . jordan . matrixToActMapList . mlp . normTrainingAndTestSet . normalizeData . outputColumns . plotActMap . plotIterativeError . plotROC . plotRegressionError . predict.rsnns . print.rsnns . rbf . rbfDDA . readPatFile . readResFile . resolveSnnsRDefine . rsnnsObjectFactory . savePatFile . setSnnsRSeedValue . snnsData . som . splitForTrainingAndTest . summary.rsnns . toNumericClassLabels . train . vectorToActMap . weightMatrix . 
Some associated R codes: RSNNS-package.R . SnnsDefines.R . SnnsRObjectFactory.R . SnnsR_createNets.R . SnnsR_extractNetInfo.R . SnnsR_masked.R . SnnsR_parser.R . SnnsR_patterns.R . SnnsR_train.R . SnnsR_util.R . art1.R . art2.R . artmap.R . assoz.R . dlvq.R . docData.R . elman.R . jordan.R . mlp.R . normalizeData.R . parser.R . rbf.R . rbfDDA.R . reg_class.R . rsnns.R . som.R . util.R .  Full RSNNS package functions and examples
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