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.

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.

Cross-correlation and most other longitudinal analyses assume that the association between 2 variables is stationary. Thus, a sample of occasions of measurement is expected to be representative of the association between variables regardless of the time of onset or number of occasions in the sample. The authors propose a method to analyze the association between 2 variables when the assumption of stationarity may not be warranted. The method results in estimates of both the strength of peak association and the time lag when the peak association occurred for a range of starting values of elapsed time from the beginning of an experiment.

Studies suggest that within-person changes in estrogen and progesterone predict changes in binge eating across the menstrual cycle. However, samples have been extremely small (maximum N = 9), and analyses have not examined the interactive effects of hormones that are critical for changes in food intake in animals. The aims of the current study were to examine ovarian hormone interactions in the prediction of within-subject changes in emotional eating in the largest sample of women to date (N = 196). Participants provided daily ratings of emotional eating and saliva samples for hormone measurement for 45 consecutive days. Results confirmed that changes in ovarian hormones predict changes in emotional eating across the menstrual cycle, with a significant estradiol x progesterone interaction. Emotional eating scores were highest during the mid-luteal phase, when progesterone peaks and estradiol demonstrates a secondary peak. Findings extend previous work by highlighting significant interactions between estrogen and progesterone that explain mid-luteal increases in emotional eating. Future work should explore mechanisms (e.g., gene-hormone interactions) that contribute to both within- and between-subject differences in emotional eating.

Historically, resilience research has been largely the purview of developmental investigators dealing with early childhood and adolescence. This research primarily focused on at-risk children who were exposed to significant and severe life adversities (e.g., extreme poverty, parental mental illness, community violence). The study of resilience in adulthood and later life, by comparison, remains largely understudied. In this article, we describe a program of research on adulthood resilience. We begin with a selective review of the broad literature on resilience, giving emphasis to the major approaches, empirical findings, and guiding principles that characterize prior studies. We then summarize our own approach to the phenomenon of resilience and illustrate select parts of our previous and ongoing studies of older adults. Findings from this research add to the growing body of empirical evidence suggesting that resilience is a common phenomenon that emerges from the coordinated orchestration of basic human adaptive processes.

A simple method for fitting differential equations to multi-wave panel data performs remarkably well in recovering parameters from underlying continuous models with as few as three waves of data. Two techniques for fitting models of intrinsic dynamics to intraindividual variability data are examined by testing these techniques' behavior in recovering the parameters from data generated by two simulated systems of differential equations. Each simulated data set contains 100 "subjects" each of whom are measured at only three points in time. A local linear approximation of the first and second derivatives of the subject's data accurately recovers the true parameters of each simulation. A statespace embedding technique for estimating the first and second derivatives does not recover the parameters as well. An optimum sampling interval can be estimated for this model as that interval at which multiple R 2 first nears its asymptotic value.

The authors present in this study a damped oscillator model that provides a direct mathematical basis for testing the notion of emotion as a self-regulatory thermostat. Parameters from this model reflect individual differences in emotional lability and the ability to regulate emotion. The authors discuss concepts such as intensity, rate of change, and acceleration in the context of emotion, and they illustrate the strengths of this approach in comparison with spectral analysis and growth curve models. The utility of this modeling approach is illustrated using daily emotion ratings from 179 college students over 52 consecutive days. Overall, the damped oscillator model provides a meaningful way of representing emotion regulation as a dynamic process and helps identify the dominant periodicities in individuals' emotions.

A dynamical systems approach was used to model the intraindividual variability in emotional well-being following conjugal loss. Well-being in a sample of 19 recently bereaved older adult widows was measured every day for 3 months. The pattern of variability of well-being was hypothesized to be an oscillating process that damps across time (i.e., large swings followed by a gradual damping). Results indicated that there was significant patterned variability in the emotional well-being adjustment that can be modeled by a linear oscillator model (R2=.77), in addition to an overall positive trend. Applying dynamical systems analyses to capture variability and subsequent well-being trajectories following spousal loss is an important step in delineating the complex adjustment to widowhood.