Feed-Forward Neural Networks and Multinomial Log-Linear Models

Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.


Software and datasets to support 'Modern Applied Statistics with S', fourth edition, by W. N. Venables and B. D. Ripley. Springer, 2002, ISBN 0-387-95457-0.

This file documents software changes since the third edition.

  • no copying of datasets even in R.
  • model.frame method for multinom (even in R).
  • nnet now uses the C interface to optim.
  • nnet.Hess has been renamed nnetHess.
  • vcov.multinom now computes the Hessian analytically (thanks to David Firth).
  • predict methods for multinom, nnet now check newdata types
  • model.frame.multinom now looks for the environment of the original formula
  • multinom has a new `model' argument defaulting to TRUE.
  • the multinom methods for add1, dropterm and anova now check for changes in the number of cases in use caused e.g. by na.action=na.omit.
  • added confint() method for multinom.
  • added logLik() method for multinom.
  • summary() for multinom now defaults to correlation=FALSE.
  • nnet() reports on 'convergence'.
  • confint.multinom() works better with a non-default 'parm'.
  • multinom() and nnet(softmax=TRUE) give an explicit error message for one-category responses.
  • the loglik() method for multinom() returns an "nobs" attribute.
  • vcov() on a "multinom" object works for fits with na.action = "na.exclude".

Reference manual

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7.3-12 by Brian Ripley, 3 years ago


Browse source code at https://github.com/cran/nnet

Authors: Brian Ripley [aut, cre, cph] , William Venables [cph]

Documentation:   PDF Manual  

Task views: Econometrics, Machine Learning & Statistical Learning, Statistics for the Social Sciences

GPL-2 | GPL-3 license

Depends on stats, utils

Suggests MASS

Imported by BaBooN, BaM, BayesTree, Biocomb, CARRoT, CORElearn, CoImp, DAMisc, EffectLiteR, EnsembleBase, Frames2, GMDH2, Hmisc, IsingSampler, LCAvarsel, LUCIDus, MEclustnet, MXM, MoEClust, Modeler, NeuralNetTools, NoiseFiltersR, OptimClassifier, Plasmode, RTextTools, RTransProb, RVAideMemoire, RecordLinkage, SIDES, SSDM, ShinyItemAnalysis, VIM, biomod2, blkbox, brglm2, car, chemmodlab, chemometrics, corHMM, cpt, effects, exprso, fRegression, factorplot, flexmix, forecast, galgo, gencve, glm.predict, gnm, hmi, hmm.discnp, hybridEnsemble, ipred, ipw, isni, jmv, kgschart, logisticRR, mExplorer, mcca, mice, mlearning, networktools, ordinalForest, pubh, pvsR, radiant.model, rasclass, reinforcelearn, rminer, semiArtificial, sigQC, simPop, sparsebnUtils, spectral.methods, spls, tsDyn, tsensembler.

Depended on by BART, BarcodingR, CBPS, GDAtools, HIest, HydeNet, ImpactIV, LOGICOIL, SQB, TBFmultinomial, abc, abn, bcROCsurface, dave, depmixS4, difNLR, elect, epiDisplay, gamlss.add, gamlss.mx, gfmR, introgress, lmQCM, partialOR, pocrm, roughrf, sodavis, synthpop.

Suggested by AER, AICcmodavg, ALEPlot, BiodiversityR, CLME, ChemometricsWithR, DynTxRegime, ExplainPrediction, GAparsimony, GSIF, HandTill2001, MASS, MachineShop, MatchIt, MuMIn, R2HTML, ROSE, Rcmdr, RcmdrPlugin.IPSUR, RcmdrPlugin.NMBU, RcmdrPlugin.pointG, SPreFuGED, SuperLearner, VRPM, aplore3, boostr, broom, caret, caretEnsemble, catdata, causaldrf, discSurv, e1071, fscaret, generalhoslem, glmulti, hnp, huxtable, lda, mboost, mi, mlDNA, mlogit, mlr, mlrMBO, mlt, mlt.docreg, nnetpredint, ordinal, pdp, performanceEstimation, plot3logit, pmml, psychomix, race, rattle, relimp, seqHMM, sparklyr, sperrorest, stablelearner, validann, vcdExtra, vip.

Enhanced by emmeans, margins, prediction, stargazer, texreg.

See at CRAN