nlr: Nonlinear Regression Modelling using Robust Methods

Non-Linear Robust package is developed to handle the problem of outliers in nonlinear regression, using robust statistics. It covers classic methods in nonlinear regression as well. It has facilities to fit models in the case of auto correlated and heterogeneous variance cases, while it include tools to detecting outliers in nonlinear regression. (Riazoshams H, Midi H, and Ghilagaber G, (2018, ISBN:978-1-118-73806-1). Robust Nonlinear Regression, with Application using R, Joh Wiley and Sons.)

Version: 0.1-1
Depends: R (≥ 3.0.0), methods
Imports: MASS, nlme, robcor, TSA, tseries, stats, GA, quantreg
Published: 2018-03-21
Author: Hossein Riazoshams
Maintainer: Hossein Riazoshams <riazihosein at gmail.com>
License: GPL-2
URL: http://www.riazoshams.com/nlr/
NeedsCompilation: no
CRAN checks: nlr results

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Reference manual: nlr.pdf
Package source: nlr_0.1-1.tar.gz
Windows binaries: r-prerel: nlr_0.1-1.zip, r-release: nlr_0.1-1.zip, r-oldrel: nlr_0.1-1.zip
OS X binaries: r-prerel: nlr_0.1-1.tgz, r-release: nlr_0.1-1.tgz

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