: Many universities provide site licenses or virtual desktop infrastructure (VDI) access to Mplus for students and faculty.
The combination add-on is the most advanced module. It contains all features of both the Mixture and Multilevel add-ons and also enables the estimation of models that handle both clustered data and latent classes in the same model. This includes advanced models such as:
For reference, the current costs for the Base Program and Combination Add-On are: Mplus Home Page Order Mplus Software - Mac - University Pricing Mplus 6.12 Base Program and Combination Add-on setup free
在 Mplus 6.12 中，组合模块不仅继承了基础程序对连续、删失、二分类、有序/无序多分类、计数等各类因变量的广泛支持，还增加了混合模型模块所特有的潜类别分析、潜类别增长模型，以及多水平模块所涵盖的跨层次中介效应、随机斜率等高级功能。此外，该版本同样支持缺失数据估计、复杂抽样校正、自助法标准误、贝叶斯分析及多重插补等实用特性。
To access Mplus entirely for free, users must utilize the official Mplus Demo Version , which is provided directly by Muthen & Muthen at no cost but features specific limitations on the number of variables you can analyze. : Many universities provide site licenses or virtual
Mplus 6.12 was designed to operate on multiple platforms, including Windows, macOS, and Linux environments. It requires minimal processing power compared to modern software, though large bootstrap simulations or intensive numerical integrations will utilize maximum CPU resources. Installation Process
Mplus Version 6.12 is a powerful statistical modeling program designed for the social, behavioral, and health sciences. The is the most advanced version, merging all capabilities of the Base Program with specialized mixture and multilevel modeling features . 💡 Core Capabilities This includes advanced models such as: For reference,
如果用户所在机构曾经购买过 Mplus 6.x 版本的正版许可，可以登录官方用户系统，免费下载对应的 6.12 升级程序。Mplus 公司曾开通线上升级系统，持有有效维护合同的用户可以随时获取 6.12 版本的安装包。
The official developers offer a free Demo version of Mplus. It includes all the features of the Base Program and the Combination Add-On. The only restriction is the size of the dataset you can analyze (typically limited to a maximum of 6 dependent variables and 2 independent variables). It is ideal for learning syntax or testing small models.
