Mplus版本8現已推出。 Mplus版本8包括對自2015年11月發布的7.4版以來發現的次要問題的更正以及以下新功能：
時間序列分析功能
N = 1次時間序列分析（用戶指南ex 6.23 - 6.28）
兩級時間序列分析（用戶指南前9.30 - 9.37）
交叉分類時間序列分析（用戶指南9.38 - 9.40）
時間序列圖（樣本值，ACF，PACF，估計因子得分）
其他新功能
隨機差異的兩級建模（用戶指南9.28,929）
兩級隨機自相關建模
具有隨機斜率和隨機方差的兩級模型的標準化
具有缺失數據的協變量的隨機斜率
兩級模型的散點圖和直方圖之間的新內部，包括樣本和模型估計的特定於簇的方法和方差
BSEM（PPPP; Hoijtink＆van deSchoot，2017; Asparouhov＆Muthén，2017）的新的後期預測性P值
以HTML格式輸出
Mplus 軟體兩個主要的功能－使用簡單和一般性的模塊化.Mplus 3 介紹許多獨一無二的特徵，包含在方程結構模型 (structural equation modeling), growth modeling, mixture modeling, multilevel modeling ，並結合這些模型化特徵

Mplus is a statistical modeling program that provides researchers with a flexible tool to analyze their data. Mplus offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results. Mplus allows the analysis of both cross-sectional and longitudinal data, single-level and multilevel data and data that come from different populations with either observed or unobserved heterogeneity. Analyses can be carried out for observed variables that are continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or combinations of these variable types. Mplus also has special features for missing data, complex survey data, and multilevel data. In addition, Mplus has extensive capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according to any of the models included in the program.
The generality of the Mplus modeling framework comes from the unique use of both continuous and categorical latent variables. Continuous latent variables are used to represent factors corresponding to unobserved constructs, random effects corresponding to individual differences in development, random effects corresponding to variation in coefficients across groups in hierarchical data, frailties corresponding to unobserved heterogeneity in survival time, liabilities corresponding to genetic susceptibility to disease, and latent response variable values corresponding to missing data. Categorical latent variables are used to represent latent classes corresponding to homogeneous groups of individuals, latent trajectory classes corresponding to types of development in unobserved populations, mixture components corresponding to finite mixtures of unobserved populations, and latent response variable categories corresponding to missing data.

Mplus Base Program
The Mplus Base Program estimates regression, path analysis, exploratory and confirmatory factor analysis (EFA and CFA), structural equation (SEM), growth, and discrete- and continuous-time survival analysis models. In regression and path analysis models, observed dependent variables can be continuous, censored, binary, ordered categorical (ordinal), counts, or a combination of these variable types. In addition, for regression analysis and path analysis for non-mediating variables, observed dependent variables can be unordered categorical (nominal). In EFA, factor indicators can be continuous, binary, ordered categorical (ordinal), or a combination of these variable types. In CFA, SEM, and growth models, observed dependent variables can be continuous, censored, binary, ordered categorical (ordinal), counts, or a combination of these variable types. Other special features include single or multiple group analysis; missing data estimation; complex survey data analysis including stratification, clustering, and unequal probabilities of selection (sampling weights); latent variable interactions and non-linear factor analysis using maximum likelihood; random slopes; individually-varying times of observation; non-linear parameter constraints; indirect effects; maximum likelihood estimation for all outcomes types; bootstrap standard errors and confidence intervals; Monte Carlo simulation facilities; and a post-processing graphics module.

Mplus Base Program and Mixture Add-On
The Mplus Base Program and Mixture Add-On contains all of the features of the Mplus Base Program. In addition, it estimates regression mixture models; path analysis mixture models; latent class analysis; latent class analysis with multiple categorical latent variables; loglinear models; finite mixture models; Complier Average Causal Effect (CACE) models; latent class growth analysis; latent transition analysis; hidden Markov models; and discrete- and continuous-time survival mixture analysis. Observed dependent variables can be continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or a combination of these variable types. Other special features include single or multiple group analysis; missing data estimation; complex survey data analysis including stratification, clustering, and unequal probabilities of selection (sampling weights); latent variable interactions and non-linear factor analysis using maximum likelihood; random slopes; individually-varying times of observation; non-linear parameter constraints; indirect effects; maximum likelihood estimation for all outcomes types; bootstrap standard errors and confidence intervals; automatic starting values with random starts; Monte Carlo simulation facilities; and a post-processing graphics module.

 Mplus Base Program and Multilevel Add-On
The Mplus Base Program and Multilevel Add-On contains all of the features of the Mplus Base Program. In addition, it estimates models for clustered data using multilevel models. These models include multilevel regression analysis, multilevel path analysis, multilevel factor analysis, multilevel structural equation modeling, multilevel growth modeling, and multilevel discrete- and continuous-time survival models. In multilevel analysis, observed dependent variables can be continuous, censored, binary, ordered categorical (ordinal), unordered categorical (nominal), counts, or a combination of these variable types. Other special features include single or multiple group analysis; missing data estimation; complex survey data analysis including stratification, clustering, and unequal probabilities of selection (sampling weights); latent variable interactions and non-linear factor analysis using maximum likelihood; random slopes; individually-varying times of observation; non-linear parameter constraints; maximum likelihood estimation for all outcomes types; Monte Carlo simulation facilities; and a post-processing graphics module.

Mplus Base Program and Combination Add-On
The Mplus Base Program and Combination Add-On contains all of the features of the Mplus Base Program and the Mixture and Multilevel Add-Ons. In addition, it includes models that handle both clustered data and latent classes in the same model, for example, two-level regression mixture analysis, two-level mixture confirmatory factor analysis (CFA) and structural equation modeling (SEM), and two-level latent class analysis, multilevel growth mixture modeling, and two-level discrete- and continuous-time survival mixture analysis. Other special features include missing data estimation; complex survey data analysis including stratification, clustering, and unequal probabilities of selection (sampling weights); latent variable interactions and non-linear factor analysis using maximum likelihood; random slopes; individually-varying times of observation; non-linear parameter constraints; maximum likelihood estimation for all outcomes types; Monte Carlo simulation facilities; and a post-processing graphics module.