Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
Version: 1.44.2 Depends: R (≥ 2.9.0) Imports: grid, MASS, grDevices, stats, utils, Deriv Suggests: testthat Published: 2019-02-07 DOI: 10.32614/CRAN.package.neuralnet Author: Stefan Fritsch [aut], Frauke Guenther [aut], Marvin N. Wright [aut, cre], Marc Suling [ctb], Sebastian M. Mueller [ctb] Maintainer: Marvin N. Wright <wright at leibniz-bips.de> BugReports: https://github.com/bips-hb/neuralnet/issues License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] URL: https://github.com/bips-hb/neuralnet NeedsCompilation: no Materials: NEWS CRAN checks: neuralnet results Documentation: Downloads: Reverse dependencies: Reverse depends: MARSANNhybrid, quarrint Reverse imports: AriGaMyANNSVR, CEEMDANML, ConvertPar, DeepLearningCausal, EventDetectR, FRI, FWRGB, gemR, Imneuron, ImNN, LilRhino, Modeler, nnfor, reddPrec, RSDA, SignacX, trackdem, traineR, WaveletML Reverse suggests: flowml, fscaret, innsight, mcboost, misspi, mlr, NeuralNetTools, NeuralSens, plotmo, qeML, TrafficBDE Reverse enhances: vip Linking:Please use the canonical form https://CRAN.R-project.org/package=neuralnet to link to this page.
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