pmlr: Penalized Multinomial Logistic Regression

Extends the approach proposed by Firth (1993) for bias reduction of MLEs in exponential family models to the multinomial logistic regression model with general covariate types. Modification of the logistic regression score function to remove first-order bias is equivalent to penalizing the likelihood by the Jeffreys prior, and yields penalized maximum likelihood estimates (PLEs) that always exist. Hypothesis testing is conducted via likelihood ratio statistics. Profile confidence intervals (CI) are constructed for the PLEs.

Version: 1.0
Published: 2010-04-02
Author: Sarah Colby, Sophia Lee, Juan Pablo Lewinger, Shelley Bull
Maintainer: Sarah Colby <colby at lunenfeld.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: pmlr results

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Reference manual: pmlr.pdf
Package source: pmlr_1.0.tar.gz
Windows binaries: r-devel: pmlr_1.0.zip, r-release: pmlr_1.0.zip, r-oldrel: pmlr_1.0.zip
OS X El Capitan binaries: r-release: pmlr_1.0.tgz
OS X Mavericks binaries: r-oldrel: pmlr_1.0.tgz

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