The mimR Package for Graphical Modelling in R

The mimR package for graphical modelling in R is introduced. We present some facilities of mimR, namely those relating specifying models, editing models, fitting models and doing model search. We also discuss the entities needed for flexible graphical modelling in terms of an ob ject structure. An example about a latent variable model is presented.

Effects of field crops on animals: Considerations with regard to design using Chlormequat-treated wheat crop as an example

There is concern whether consuming products based on crop from Chlormequat-treated fields can cause reproduction problems in animals and humans. An experiment is presently being conducted to investigate this using the pig as a model. Considerations with regard to experimental design when investigating whether differently treated crop can affect animal/human biology is discussed. Only about half of the data are presently available. A preliminary survey of these data does not show clear differences between Chlormequat-treated and organic non-treated wheat with regard to reproduction performance of pigs

Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R

This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows.

A Common Platform for Graphical Models in R: The gRbase Package

The gRbase package is intended to set the framework for computer packages for data analysis using graphical models. The gRbase package is developed for the open source language, R, and is available for several platforms. The package is intended to be widely extendible and flexible so that package developers may implement further types of graphical models using the available methods. The gRbase package consists of a set of S version 3 classes and associated methods for representing data and models. The package is linked to the dynamicGraph package (Badsberg 2005), an interactive graphical user interface for manipulating graphs. In this paper, we show how these building blocks can be combined and integrated with inference engines in the special cases of hierarchical loglinear models. We also illustrate how to extend the package to deal with other types of graphical models, in this case the graphical Gaussian models.

Inference in Graphical Gaussian Models with Edge and Vertex Symmetries with the gRc Package for R

In this paper we present the R package gRc for statistical inference in graphical Gaussian models in which symmetry restrictions have been imposed on the concentration or partial correlation matrix. The models are represented by coloured graphs where parameters associated with edges or vertices of same colour are restricted to being identical. We describe algorithms for maximum likelihood estimation and discuss model selection issues. The paper illustrates the practical use of the gRc package.

SASWeave: Literate Programming Using SAS

SASweave is a collection of scripts that allow one to embed SAS code into a LATEX document, and automatically incorporate the results as well. SASweave is patterned after Sweave, which does the same thing for code written in R. In fact, a document may contain both SAS and R code. Besides the convenience of being able to easily incorporate SAS examples in a document, SASweave facilitates the concept of "literate programming": having code, documentation, and results packaged together. Among other things, this helps to ensure that the SAS output in the document is in concordance with the code.

The R Package geepack for Generalized Estimating Equations

This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.

The mimR Package for Graphical Modelling in R

The mimR package for graphical modelling in R is introduced. We present some facilities of mimR, namely those relating specifying models, editing models, fitting models and doing model search. We also discuss the entities needed for flexible graphical modelling in terms of an ob ject structure. An example about a latent variable model is presented