Main Article Content
Structural equation mixture modeling (SEMM) has become a standard procedure in latent variable modeling over the last two decades (Jedidi, Jagpal, and DeSarbo 1997b; Muthén and Shedden 1999; Muthén 2001, 2004; Muthén and Asparouhov 2009). SEMM was proposed as a technique for the approximation of nonlinear latent variable relationships by finite mixtures of linear relationships (Bauer 2005, 2007; Bauer, Baldasaro, and Gottfredson 2012). In addition to this semiparametric approach to nonlinear latent variable modeling, there are numerous parametric nonlinear approaches for normally distributed variables (e.g., LMS in Mplus; Klein and Moosbrugger 2000). Recently, an additional semiparametric nonlinear structural equation mixture modeling (NSEMM) approach was proposed by Kelava, Nagengast, and Brandt (2014) that is capable of dealing with nonnormal predictors. In the nlsem package presented here, the SEMM, two distribution analytic (QML and LMS) and NSEMM approaches can be specified and estimated. We provide examples of how to use the package in the context of nonlinear latent variable modeling.