Dr Heather Turner

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gnm: Generalized Nonlinear Models

These models are like generalized linear models (linear regression, logistic regression, log-linear models, etc.) but may also include one or more nonlinear terms in the predictor function (i.e., the right hand side of the regression equation).

For an introduction, see the R News article (for regular R users) or the Statistical Computing & Graphics Newsletter article (for those less familiar with R). The Talks page has slides from various talks related to the package, covering both technical details and applications. More information, including workshop slides, is available on the gnm webpage.

BradleyTerry2: Bradley-Terry Models

Bradley-Terry models are for modelling pair comparison data, e.g. to predict the winner in a paired contest based on player ability. This package covers models in which the ability or worth is modelled by covariates.

These slides from useR! 2010 introduce the package, while a more detailed guide can be found in this JSS paper. The package repository is hosted on R-Forge.

gslcca: Generalized Semi-linear Canonical Correlation Analysis

Generalized Semi-linear Canonical Correlation Analysis (GSLCCA) estimates the parameters of a given nonlinear model to maximize the correlation with a linear combination of multiple response variables. This method was developed to characterize EEG power spectra under different treatment regimens in research and development projects at Pfizer.

The package is still in development. Further details, including the current development version can be found via the gslcca project homepage on R Forge.

vcdExtra: Extra methods for visualising categorical data

This package complements the vcd package for visualising categorical data, providing additional methods and examples.

See the vcdExtra webpage on R-Forge for more information.

biclust: Biclustering Methods

Although I am not a developer of biclust, the plaid model algorithm in this package is based on algorithm and R code I developed during my PhD as described in this CSDA article.