The aim of this study is to present overview on R packages for structural equation modeling. Structural equation modeling, a statistical technique for testing and estimating causal relations using an amalgamation of statistical data and qualitative causal hypotheses, allow both confirmatory and exploratory modeling, meaning they are matched to both hypothesis testing and theory development. R project or R language, a free and popular programming language and computer software surroundings for statistical computing and graphics, is popularly used among statisticians for developing statistical computer software and data analysis. The major finding is that it is necessary to build excellent and enough structural equation modeling packages for R users to do research. Numerous packages for structural equation modeling of R project are introduced in this study and most of them are enclosed in the Comprehensive R Archive Network task view Psychometrics.

Time series analysis consists of approaches for analysing time series data so thatimportant information and other features can be isolated from the data. Time series forecasting is the use of a model to predict perspective values on the basis of previouly observed values by a model. Statisticians generally use R project or R language, a free and popular programming language and computer software environment for statistical computing and graphics, for developing statistical computer software and data analysis. Plenty of time series display cyclic variation significant as seasonality, periodic variation, or periodic fluctuations in statistics. This study introducesabundant functions in the R packages TSA, marls, depersonalize and season for analyzing seasonal processes of time series, are introduced in this study. Note that R packages marls, depersonalize and season are included in the comprehensive R archive network task view TimeSeries.