The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly-written CSOLNP. Entire new methodologies such as Item Factor analysis (IRT) and State-space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

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This paper introduces an Item Factor Analysis (IFA) module for OpenMx, a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation and manipulation of models. Modular organization of the source code facilitates the easy addition of item models, item parameter estimation algorithms, optimizers, test scoring algorithms, and fit diagnostics all within an integrated framework. Three short example scripts are presented for fitting item parameters, latent distribution parameters, and a multiple group model. The availability of both IFA and structural equation modeling in the same software is a step toward the unification of these two methodologies.

This is the first study to the authors' knowledge to use a social cognition paradigm to reveal improved left medial prefrontal cortex activation in schizophrenia after recovery from an acute episode. These results suggest that restored left medial prefrontal cortex activation may mediate improvement of insight and social functioning in patients with schizophrenia.

Modafinil did not improve cognitive control in all schizophrenia patients. Increased activation in the dorsolateral prefrontal cortex and in neuropsychological performance were observed in patients with suboptimal baseline function.

Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete-and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-tolearn model specification functions in R. We present the mathematical and computational bases used by the dynr R package, and present two illustrative examples to demonstrate the unique features of dynr.

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A concept that has been bouncing around the psychological literature for some time is that of resilience, the idea that individuals can exhibit positive adaptation in the process of experiencing adverse life events. Simply put, one might say resilient people bounce back from negative events. The New Shorter Oxford English Dictionary (1993) contains the following definitions of resilience:1 a The action or act of rebounding or springing back. b Recoil from something; revolt. 2 Elasticity; spec. the amount of energy per unit volume that a material absorbs when subjected to strain. 3 The ability to recover readily from, or resist being affected by, a setback, illness, etc. (p. 2562) These definitions constitute an interesting overlap of physical and psychological meanings. This overlap creates an intentional metaphor of elasticity when used to describe a short-term, intraindividual psychological process. However, positive adaptation and elasticity may have subtly differing meanings.In this chapter, we examine some models that can describe two physical properties, elasticity and damping, that are used to describe resilient physical systems. We propose that models such as these are important to consider if one wishes to make a claim of construct validity for the overlap between the physical and psychological meanings of resilience. In other words, our argument is that if we wish to measure how someone "bounces back," we need models that can parameterize the bounce. If we fit models describing components of physical elasticity to intraindividual psychological adaptation in the presence of stress and find that parts of the model do not provide explanatory power, then we would like to know what parts of the physical elasticity metaphor for resilience should be modified or discarded. Finally, by taking the physical elasticity metaphor seriously, we introduce a language for expressing differences between models that can help clarify our thinking about different forms of intraindividual adaptation to stress.