# Some Publications

Brandt, H. (accepted for publication). A more efficient causal mediator model without the no-unmeasured-confounder assumption. Multivariate Behavioral Research.

Brandt, H., Umbach, N., Kelava, A., & Bollen, K. A. (accepted for publication). Comparing estimators for latent interaction models under structural and distributional misspecifications. Psychological Methods.

Kelava, A. & Brandt, H. (2019). A Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling, 26, 509-528.

Brandt, H., Cambria, J., & Kelava, A. (2018). An adaptive Bayesian lasso approach with spike-and-slab priors to identify linear and interaction effects in structural equation models. Structural Equation Modeling, 25, 946-960.

Umbach, N., Naumann, K., Brandt, H., & Kelava, A. (2017). Fitting nonlinear structural equation mixture models in R with package nlsem. Journal of Statistical Software, 7, 1–20.

Brandt, H. & Klein, A. G. (2015). A heterogeneous growth curve model for non-normal data. Multivariate Behavioral Research, 50, 416–435.

Brandt, H., Umbach, N., & Kelava, A. (2015). The standardization of nonlinear effects in direct and indirect applications of structural equation mixture models. Frontiers in Psychology (Quantitative Psychology and Measurement), 6:1813.

Brandt, H., Kelava, A., & Klein, A. G. (2014). A simulation study comparing recent approaches for the estimation of nonlinear effects in SEM under the condition of non-normality. Structural Equation Modeling, 21, 181–195.

Kelava, A. & Brandt, H. (2014). A general nonlinear multilevel structural equation mixture model. Frontiers in Psychology (Quantitative Psychology and Measurement), 5:748.

Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for non-normally distributed latent predictor variables. Structural Equation Modeling, 21, 468–481.

Kelava, A. & Brandt, H. (2009). Estimation of nonlinear SEM with the sem package. Review of Psychology, 16, 123–131.