monmlp: Monotone Multi-Layer Perceptron Neural Network
Version 1.1.4

Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints.

Browse man pages Browse package API and functions Browse package files

AuthorAlex J. Cannon
Date of publication2017-03-23 21:05:06 UTC
MaintainerAlex J. Cannon <alex.cannon@canada.ca>
LicenseGPL-2
Version1.1.4
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("monmlp")

Man pages

gam.style: GAM-style effects plots for interpreting MONMLP models
linear: Identity function
linear.prime: Derivative of the linear function
logistic: Logistic sigmoid function
logistic.prime: Derivative of the logistic sigmoid function
monmlp.fit: Fit a MONMLP model or an ensemble of MONMLP models
monmlp-package: Monotone Multi-Layer Perceptron Neural Network
monmlp.predict: Make predictions from a fitted MONMLP model
tansig: Hyperbolic tangent sigmoid function
tansig.prime: Derivative of the hyperbolic tangent function

Functions

gam.style Man page Source code
linear Man page Source code
linear.prime Man page Source code
logistic Man page Source code
logistic.prime Man page Source code
monmlp Man page
monmlp-package Man page
monmlp.cost Source code
monmlp.fit Man page Source code
monmlp.grad Source code
monmlp.initialize Source code
monmlp.nlm Source code
monmlp.predict Man page Source code
monmlp.reshape Source code
tansig Man page Source code
tansig.prime Man page Source code

Files

NAMESPACE
R
R/tansig.R
R/monmlp.reshape.R
R/logistic.prime.R
R/tansig.prime.R
R/monmlp.cost.R
R/monmlp.nlm.R
R/monmlp.grad.R
R/gam.style.R
R/monmlp.fit.R
R/linear.R
R/monmlp.predict.R
R/logistic.R
R/linear.prime.R
R/monmlp.initialize.R
MD5
DESCRIPTION
man
man/gam.style.Rd
man/monmlp-package.Rd
man/logistic.prime.Rd
man/tansig.Rd
man/logistic.Rd
man/tansig.prime.Rd
man/linear.Rd
man/monmlp.predict.Rd
man/monmlp.fit.Rd
man/linear.prime.Rd
monmlp documentation built on May 19, 2017, 12:39 p.m.