使用期限永久
许可形式单机和网络版
原产地美国
介质下载
适用平台windows,mac
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Alignment for ESEM and SEM (Asparouhov & Muthen, 2022a), including models with cross-loadings; ML and WLSMV estimators with Delta and Theta parameterizations; and continuous, binary, and ordinal variables.
Bayes estimation for residual SEM (RSEM; Asparouhov & Muthen 2022b) with count, nominal, and logit-binary distal outcomes.

Mplus provides both Bayesian and frequentist inference. Bayesian analysis uses Markov chain Monte Carlo (MCMC) algorithms. Posterior distributions can be monitored by trace and autocorrelation plots. Convergence can be monitored by the Gelman-Rubin potential scaling reduction using parallel computing in multiple MCMC chains. Posterior predictive checks are provided.

Automatic checking of whether two structural equation models are nested or equivalent (Bentler & Satorra. 2010) implemented and expanded to multiple group models and the weighted least squares estimators (Asparouhov & Muthén, 2018c, (Download scripts))

Mplus provides multiple imputation of missing data using Bayesian analysis (Rubin, 1987; Schafer, 1997). Both the unrestricted H1 model and a restricted H0 model can be used for imputation.
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