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Novel Approaches in Mixture Modeling
–Gitta Lubke & Kevin J. Grimm [] []

An Empirical Assessment of the Sensitivity of Mixture Models to Changes in Measurement
–Veronica T. Cole, Daniel J. Bauer, Andrea M. Hussong & Michael L. Giordano [] []

Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling
–Katherine E. Masyn [] []

Using Bayesian Statistics to Model Uncertainty in Mixture Models: A Sensitivity Analysis of Priors
–Sarah Depaoli, Yuzhu Yang & John Felt [] []

Power and Type I Error of Local Fit Statistics in Multilevel Latent Class Analysis
–Erwin Nagelkerke, Daniel L. Oberski & Jeroen K. Vermunt [] []

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
–Gitta H. Lubke, Ian Campbell, Dan McArtor, Patrick Miller, Justin Luningham & Stéphanie M. van den Berg [] []

Model Selection in Finite Mixture Models: A k-Fold Cross-Validation Approach
–Kevin J. Grimm, Gina L. Mazza & Pega Davoudzadeh [] []

Dynamic Latent Class Analysis
–Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén [] []

A Comparison of Methods for Uncovering Sample Heterogeneity: Structural Equation Model Trees and Finite Mixture Models
–Ross Jacobucci, Kevin J. Grimm & John J. McArdle [] []

Pattern Mixture Models for Quantifying Missing Data Uncertainty in Longitudinal Invariance Testing
–Sonya K. Sterba [] []

Studying the Strength of Prediction Using Indirect Mixture Modeling: Nonlinear Latent Regression with Heteroskedastic Residuals | Open Access
–Johanna M. de Kort, Conor V. Dolan, Gitta H. Lubke & Dylan Molenaar [] []

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