OpenMx is free, full–featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS–X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced — these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.

This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order 'relatives' of a high-order term (e.g., main effects marginal to an interaction) are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model. Background and MotivationThis paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy (in the sense of Nelder, 1977), such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays.I assume that readers are familiar with R, which is a free, open-source implementation of the S statistical programming language and computing environment. Extensive information on R is available at . The software described in the paper is distributed as the effects package for R, available on the Comprehensive R Archive Network (CRAN) at .The general approach implemented in the effects package is a modest extension of methods introduced by Fox (1987). Similar, if less general, ideas have a long history, dating at least to Fisher's (1936) "adjusted means" in the analysis of covariance. Goodnight and Harvey's (1978) "least-squares means" in analysis of variance and covariance, and Searle, Speed, and Milliken's (1980) "estimated population marginal means" are other examples, though all of these approaches are restricted to interactions among factors (i.e., categorical predictors) in a linear model. Recently King, Tomz, and Wittenberg (2000) and Tomz, Wittenberg, and King (2003) have presented similar ideas, but their approach, based on Monte-Carlo simulation of a model, is much more complicated than the one discussed here.To motivate effect displays, consider the following data on police treatment of individuals arrested in Toronto for simple possession of small quantities of marijuana. (The data discussed here are part of a larger data set featured in a series of articles in the Toronto Star newspaper.) Under these circumstances police have the option of releasing an arrestee with a summons to appear in court -similar to a traffic ticket; altern...

Unlike S-PLUS, R does not incorporate a statistical graphical user interface (GUI), but it does include tools for building GUIs. Based on the tcltk package (which furnishes an interface to the Tcl/Tk GUI toolkit), the Rcmdr package provides a basic-statistics graphical user interface to R called the "R Commander." The design objectives of the R Commander were as follows: to support, through an easy-to-use, extensible, crossplatform GUI, the statistical functionality required for a basic-statistics course (though its current functionality has grown to include support for linear and generalized-linear models, and other more advanced features); to make it relatively difficult to do unreasonable things; and to render visible the relationship between choices made in the GUI and the R commands that they generate. The R Commander uses a simple and familiar menu/dialog-box interface. Top-level menus include File, Edit, Data, Statistics, Graphs, Models, Distributions, Tools, and Help, with the complete menu tree given in the paper. Each dialog box includes a Help button, which leads to a relevant help page. Menu and dialog-box selections generate R commands, which are recorded in a script window and are echoed, along with output, to an output window. The script window also provides the ability to edit, enter, and re-execute commands. Error messages, warnings, and some other information appear in a separate messages window. Data sets in the R Commander are simply R data frames, and can be read from attached packages or imported from files. Although several data frames may reside in memory, only one is "active" at any given time. There may also be an active statistical model (e.g., an R lm or glm object). The purpose of this paper is to introduce and describe the use of the R Commander GUI; to describe the design and development of the R Commander; and to explain how the R Commander GUI can be extended. The second part of the paper (following a brief introduction) can serve as an introductory guide for students who will use the R Commander Keywords: statistical GUI, statistical software, statistical education, R language. 2The R Commander: A Basic-Statistics GUI to R Background and motivationR (Ihaka and Gentleman 1996; R Core Development Team 2004) is a free, open-source implementation of the S statistical computing language and programming environment. R is a command-driven system: One normally specifies a statistical analysis in R by typing commands-that is, statements in the S language that are executed by the R interpreter. S-PLUS (a commercial implementation of the S language), also incorporates a graphical user interface (GUI) to much of its statistical functionality.In my opinion, a GUI for statistical software is a mixed blessing: On the one hand, a GUI does not require that the user remember the names and arguments of commands, and decreases the chances of syntax and typing errors. These characteristics make GUIs particularly attractive for introductory, casual, or infrequent use of software.On the other ...

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The multivariate linear model is