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8.8版最新更新（2022年4月26日）
Mplus 8.8 版现已推出。Mplus 版本 8.8 包括对自 2021 年 11 月版本 8.7 发布以来发现的小问题的更正以及以下新功能：
8.7版最新更新（2021年11月1日）
Mplus 8.7 版现已推出。 Mplus 8.7 版包括对 2021 年 3 月 8.6 版发布以来发现的小问题的更正以及以下新功能：
- 观察变量和潜在变量的 SEM 残差之间的回归。 版本 8.1 中引入的新语言用于使用 LAGGED 选项和贝叶斯估计量的 RDSEM 滞后变量残差之间的回归，已扩展到最大似然、加权最小二乘法和贝叶斯估计量的常规单水平模型。 这包括具有残差之间自回归的增长模型和面板数据的随机截距交叉滞后模型（RI-CLPM 和 RI-ARMA）。 Asparouhov & Muthen (2021a) 中描述了新的建模功能，并在 Mplus Web Talk No. 4 中进一步说明。
- RI-CLPM 可使用新的贝叶斯算法获得用于二元和有序分类变量的 RI-CLPM，包括访问 Theta 矩阵的残差参数 (Asparouhov & Muthen, 2021a)。
- RDSEM 残差建模已扩展为包括滞后变量和同时发生的变量（Asparouhov & Muthen (2021a)）。
- TECH10 现在可用于贝叶斯和 WLSMV，具有响应模式的标准化残差和单变量和双变量表，用于使用分类变量进行更全面的模型拟合评估。
- 贝叶斯的 TECH 10 包括对带有分类变量的单变量和双变量表的 Pearson 后验预测检查 (Asparouhov & Muthen (2021a))。
- 添加了对有序变量具有局部依赖性的贝叶斯 LCA 建模（Asparouhov & Muthen，2021a）。
对于最大似然估计量，添加了一种用于两水平建模的改进卡方检验，其中水平间残差为零（Asparouhov & Muthen，2021b）。
- 对于带有贝叶斯估计器的 TYPE = TWOLEVEL，使用 SAVEDATA 命令的 FILE 选项的 * 设置保存多个水平间因子分数（随机效应）的绘制，例如，FILE = between*.dat；
- VARIABLE 命令的 USEOBSERVATIONS 选项的新设置，用于排除没有集群内变化的集群。 这对于样本的一部分不随时间变化的纵向数据很有用。 USEOBSERVATIONS = 变量 EQ _CLUSVAR;
- 新的绘图选项可用于时间序列分析，包括允许星期几效应的阶跃函数、多个循环插入以及每个时间点的直方图。
8.6版最新更新（2021年3月22日）
Mplus版本8.6现在可用。 Mplus 8.6版包含对自2020年11月8.5版发布以来发现的小问题的更正，以及以下新功能：
- 增加了输出功能和新的潜在转换分析计算器，这使解释分析结果更加方便，尤其是在评估协变量对转换概率的影响时。 计算器位于Mplus编辑器的Mplus菜单中。 Mplus Web演讲2：使用Mplus进行潜在转换分析和随机拦截潜在转换分析以及技术附录LTA解释概率，奇数和奇数比率结果的新功能已在其中进行了描述。
- 修订的Mplus Web Note 21第9节中介绍了潜在迁移分析的简化三步估算
- 当存在多个潜在类变量时，例如在潜在转换分析中，每个潜在类变量的熵
- 对DATA命令的TYPE = IMPUTATION选项的贝叶斯估计。 这将为非对称CI提供估算数据。 新功能在技术附录Mplus中使用贝叶斯估计器分析插补数据中进行了描述
- 使用计数变量提高贝叶斯估计的速度
- 用于二进制变量的logit链接的贝叶斯估计的多处理器计算
- 保存使用Analysis命令的BOOTSTRAP选项生成的数据集和参数估计值。 这有助于诊断引导绘图的问题分析。 技术附录 Bootstrap计算问题中介绍了此新功能。
- 使用VARIABLE命令的KNOWNCLASS选项时，采样权重的缩放比例。 这将权重缩放到每个已知类中的总数，这也是使用多组分析完成的
8.5版最新更新（2020年11月16日）
Mplus Version 8.5 is now available. Mplus Version 8.5 includes corrections to minor problems that have been found since the release of Version 8.4 in November 2019 as well as the following new features:
- Automatic reordering of latent classes for mixture models such as LCA and LTA
- Bayes estimation for count, nominal, and binary variables using a logit link for both real data analysis and Monte Carlo simulation studies for single-level, two-level, and mixture models with a single categorical latent variable. The Bayesian estimation is particularly valuable in cases with many latent variables where maximum-likelihood would be intractable due to too heavy numerical integration. The logit link for binary variables makes odds ratio interpretations available also with Bayes(Asparouhov & Muthén, 2020)(Paper scripts).
- Bayes estimation of mixture models with categorical latent variable regression on observed variables
- Data imputation for count, nominal, and binary variables with a logit link
- Multilevel data saving
- BCH 3-step analysis with ALGORITHM=INTEGRATION and multiple C
- Time series plots with confidence intervals and loop plot insert for Means over subjects plot
- Estimated probability plots for Bayes with count and nominal
- Conditional plots for Bayes continuous and categorical: Adjusted estimated means, conditional estimated probabilities, etc.
The Version 8.5 Mplus Language Addendum can be found on the website along with the Mplus Version 8 User’s Guide.
8.4版最新更新
Mplus 8.4版,2019年11月19日
Mplus Version 8.4 is now available. Mplus Version 8.4 includes corrections to minor problems that have been found since the release of Version 8.3 in April 2019 as well as the following new features:
- Significant speed improvements for computationally demanding mixture models, particularly with multiple latent class variables such as with Latent Transition Analysis and Random Intercept Latent Transition Analysis. These speed improvements are achieved using a new approach to the random starts perturbation options STARTS and STSCALE as well as new algorithms. These developments are described in Random Starting Values and Multistage Optimization. A Section 3 timing table for 5 mixture examples shows the large speed improvements in version 8.4 relative to 8.3.
- Substantially simplified output for mixture models with multiple latent class variables
- Expanded Bayesian fit statistics in 3 areas: improved posterior predictive p-values (PPP) when there are missing data, Bayesian CFI/TLI/RMSEA including confidence intervals, and a Bayesian version of the Wald test of parameter restrictions using the MODEL TEST command.
- Improved algorithms for ML estimation of large multivariate three-level models with missing data
8.3版最新更新
Mplus Version 8.3, April 30, 2019
Mplus Version 8.3 is now available. Mplus Version 8.3 includes corrections to minor problems that have been found since the release of Version 8.2 in November 2018 as well as the following new features:
- Significant speed improvements for Bayesian computations using a new parallelized computing approach (Asparouhov & Muthén, 2019a). In addition, Bayesian algorithms have been reorganized to produce faster computations. The resulting speed-up is particularly noteworthy for multilevel and time series (DSEM/RDSEM) analyses where in some cases the computing time is cut in half and even cut down to 20% of the version 8.2 timings. Timing tables for a variety of examples are presented in the technical documentation. (Download timing examples - i9-9900k, Proc=4).
- Bayesian estimation of twolevel models with latent variable interactions using the XWITH option (Asparouhov & Muthén, 2019b). This is especially helpful for models with moderation where maximum-likelihood estimation is problematic due to the need for numerical integration. Download the Section 4 Bayes examples
- The BOOTSTRAP option is available for TYPE=COMPLEX (Bootstrap technical appendix). In addition to being available for TYPE=GENERAL, the BOOTSTRAP option is available for TYPE=GENERAL when the WEIGHT option is used.
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