lslx is a package for fitting semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with lasso or minimax concave penalty (MCP) developed by Huang, Chen, and Weng (2017). In this semi-confirmatory method, an SEM model is distinguished into two parts: a confirmatory part and an exploratory part. The confirmatory part includes all of the freely estimated parameters and fixed parameters that are allowed for theory testing. The exploratory part is composed by a set of penalized parameters describing relationships that cannot be clearly determined by available substantive theory. By implementing a sparsity-inducing penalty and choosing an optimal penalty level, the relationships in the exploratory part can be efficiently determined by the sparsity pattern of these penalized parameters.

lslx can be also seen as a package for conducting usual SEM with several state-of-art inference methods, including sandwich standard error formula, mean-adjusted likelihood ratio test, and two stage method with auxiliary variables for missing data. lslx also supports multi-group analysis for evaluating group heterogeneity (Huang, 2018) and penalized least squares for SEM with ordianl data under delta parameterization. For now, the major limitations of lslx are that (1) it cannot impose linear or non-linear constraints for coefficients; (2) it cannot make valid inference under clustered or dependent data.

A brief summary of functionality of lslx can be found in Package Features. To learn how to use lslx, please refer to Tutorial. A simple comparison of packages for SEM with PL, including lslx, lsl, and regsem, is described in Comparison.

lslx is an R6ClassGenerator for constructing an lslx object that has methods for fitting semi-confirmatory SEM. In a simplest case, the use of lslx involves three major steps

1. Initialize a new lslx object by specifying a model and importing a data set.

r6_lslx <- lslx$new(model, data)

2. Fit the specified model to the imported data with specified fitting control.

r6_lslx$fit(penalty_method, lambda_grid, delta_grid)

3. Summarize the fitting results with specified selector.

r6_lslx$summarize(selector)

To install the CRAN version, just simply type

install.packages("lslx", dependencies = TRUE)

If you find any bug, please post it on issue.